Learning Bayesian Statistics

You know I love epistemology — the study of how we know what we know. It was high time I dedicated a whole episode to this topic. And what better guest than Aubrey Clayton, the author of the book Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. I’m in the middle of reading it, and it’s a really great read!Aubrey is a mathematician in Boston who teaches the philosophy of probability and statistics at the Harvard Extension School. He holds a PhD in mathematics from the University of California, Berkeley, and his writing has appeared in Pacific Standard, Nautilus, and the Boston Globe.We talked about what he deems “a catastrophic error in the logic of the standard statistical methods in almost all the sciences” and why this error manifests even outside of science, like in medicine, law, public policy, etc.But don’t worry, we’re not doomed — we’ll also see where we go from there. As a big fan of E.T Jaynes, Aubrey will also tell us how this US scientist influenced his own thinking as well as the field of Bayesian inference in general.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Alejandro Morales, Tomáš Frýda, Ryan Wesslen and Andreas Netti.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Aubrey's website: https://aubreyclayton.com/Aubrey on Twitter: https://twitter.com/aubreyclaytonBernoulli's Fallacy: https://aubreyclayton.com/bernoulliAubrey's probability theory lectures based on E.T Jayne's work: https://www.youtube.com/playlist?list=PL9v9IXDsJkktefQzX39wC2YG07vw7DsQ_What Society Gets Wrong About Statistics: https://www.youtube.com/watch?v=fDulF2MzsIUThe Prosecutor's Fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacyThe Theory That Would Not Die -- How Bayes' Rule Cracked the Enigma Code:

Folks, this is the 50th episode of LBS — 50th! I never would have thought that there were so many Bayesian nerds in the world when I first interviewed Osvaldo Martin more than 2 years ago. To celebrate that random, crazy adventure, I wanted to do a special episode at any random point, and so it looks like it’s gonna be #50! This episode is special by its guest, not its number — although my guest knows a thing or two about numbers. Most recently, he wrote the book Covid by Numbers.A mathematical statistician dedicated to helping the general public understand risk, uncertainty and decision-making, he’s the author of several books on the topic actually, including The Art of Statistics. You may also know him from his podcast, Risky Talk, or his numerous appearances in newspapers, radio and TV shows.Did you guess who it is?Maybe you just know him as the reigning World Champion in Loop – a version of pool played on an elliptical table – and are just discovering now that he is a fantastic science communicator – something that turns out to be especially important for stats education in times of, let’s say, global pandemic for instance.He holds a PhD in Mathematical Statistics from the University of London and has been the Chair of the Winton Centre for Risk and Evidence Communication at Cambridge University since 2016. He was also the President of the famous Royal Statistical Society in 2017-2018.Most importantly, he was featured in BBC1’s Winter Wipeout in 2011 – seriously, go check it out on his website; it’s hilarious.So did you guess it yet? Yep, my guest for this episode is no other than Sir David Spiegelhalter — yes, there are Bayesian knights!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Alejandro Morales and Tomáš Frýda.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:David's website: http://www.statslab.cam.ac.uk/~david/David on Twitter: https://twitter.com/d_spiegelThe Art of Statistics: https://dspiegel29.github.io/ArtofStatistics/Risky Talk podcast: https://riskytalk.libsyn.com/Winton Centre for Risk and Evidence Communication:

It’s been a while since I did an episode about sports analytics, right? And you know it’s a field I love, so… let’s do that!For this episode, I was happy to host Ehsan Bokhari, not only because he’s a first-hour listener of the podcast and spread the word about it whenever he can, but mainly because he knows baseball analytics very well!Currently Senior Director of Strategic Decision Making with the Houston Astros, he previously worked there as Senior Director of Player Evaluation and Director of R&D. And before that, he was Senior Director at the Los Angeles Dodgers from the 2015 to the 2018 season.Among other things, we talked about what his job looks like, how Bayesian the field is, which pushbacks he gets, and what the future of baseball analytics look like to him.Ehsan also has an interesting background, coming from both psychology and mathematics. Indeed, he received a PhD in quantitative psychology and an MS in statistics at the University of Illinois in 2014.Maybe most importantly, he loves reading non-fiction and spending time with his almost three-year-old son — who he read Bayesian Probability for Babies to, of course.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, and Alejandro Morales.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Ehsan on LinkedIn: https://www.linkedin.com/in/ebokhari/Bayesian Bagging to Generate Uncertainty Intervals -- A Catcher Framing Story: https://www.baseballprospectus.com/news/article/38289/bayesian-bagging-generate-uncertainty-intervals-catcher-framing-story/ Jim Albert's Bayesball blog: https://bayesball.github.io/Simulation of empirical Bayesian methods, using baseball statistics: http://varianceexplained.org/r/simulation-bayes-baseball/Detection and Characterization of Cluster Substructure -- Fuzzy c-Lines: https://epubs.siam.org/doi/abs/10.1137/0140029Tensor rank decomposition:

In episode 40, we already got a glimpse of how useful Bayesian stats are in the speech and communication sciences. To talk about the frontiers of this field (and, as it happens, about best practices to make beautiful plots and pictures), I invited TJ Mahr on the show.A speech pathologist turned data scientist, TJ earned his PhD in communication sciences and disorders in Madison, Wisconsin. On paper, he was studying speech development, word recognition and word learning in preschoolers, but over the course of his graduate training, he discovered that he really, really likes programming and working with data – we’ll of course talk about that in the show!In short, TJ wrangles data, crunches numbers, plots pictures, and fits models to study how children learn to speak and communicate. On his website, he often writes about Bayesian models, mixed effects models, functional programming in R, or how to plot certain kinds of data.He also got very into the deck-building game “Slay the Spire” this year, and his favorite youtube channel is a guy who restores paintings.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, and Luis Iberico.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:TJ's website: https://www.tjmahr.com/TJ on Twitter: https://twitter.com/tjmahrTJ on GitHub: https://github.com/tjmahrLBS #40, Bayesian Stats for the Speech & Language Sciences: https://www.learnbayesstats.com/episode/40-bayesian-stats-speech-language-sciences-allison-hilger-timo-roettgerRandom Effects and Penalized Splines: https://www.tjmahr.com/random-effects-penalized-splines-same-thing/Bayes’s theorem in three panels: https://www.tjmahr.com/bayes-theorem-in-three-panels/Another mixed effects model visualization:

The field of physics has brought tremendous advances to modern Bayesian statistics, especially inspiring the current algorithms enabling all of us to enjoy the Bayesian power on our own laptops.I did receive some physicians already on the show, like Michael Betancourt in episode 6, but in my legendary ungratefulness I hadn’t dedicated a whole episode to talk about physics yet.Well that’s now taken care of, thanks to JJ Ruby. Apart from having really good tastes (he’s indeed a fan of this very podcast), JJ is currently a postdoctoral fellow for the Center for Matter at Atomic Pressures at the University of Rochester, and will soon be starting as a Postdoctoral Scholar at Lawrence Livermore National Laboratory, a U.S. Department of Energy National Laboratory.JJ did his undergraduate work in Astrophysics and Planetary Science at Villanova University, outside of Philadelphia, and completed his master’s degree and PhD in Physics at the University of Rochester, in New York.JJ studies high energy density physics and focuses on using Bayesian techniques to extract information from large scale physics experiments with highly integrated measurements.In his freetime, he enjoys playing sports including baseball, basketball, and golf.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin and Cameron Smith.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Center for Matter at Atomic Pressures: https://www.rochester.edu/cmap/Laboratory for Laser Energetics: https://www.lle.rochester.edu/index.php/about-the-laboratory-for-laser-energetics/Lawrence Livermore National Laboratory: https://www.llnl.gov/JJ's thesis -- Bayesian Inference of Fundamental Physics at Extreme Conditions: https://www.lle.rochester.edu/media/publications/documents/theses/Ruby.pdfRecent Fusion Breakthrough: https://www.llnl.gov/news/national-ignition-facility-experiment-puts-researchers-threshold-fusion-ignitionLBS #6, A principled Bayesian...

You wanna know something funny? A sentence from this episode became a meme. And people even made stickers out of it! Ok, that’s not true. But if someone could pull off something like that, it would surely be Chelsea Parlett-Pelleriti.Indeed, Chelsea’s research focuses on using statistics and machine learning on behavioral data, but her more general goal is to empower people to be able to do their own statistical analyses, through consulting, education, and, as you may have seen, stats memes on Twitter.A full-time teacher, researcher and statistical consultant, Chelsea earned an MsC and PhD in Computational and Data Science in 2021 from Chapman University. Her courses include R, intro to programming (in Python), and data science.In a nutshell, Chelsea is, by her own admission, an avid lover of anything silly or statistical. Hopefully, this episode turned out to be both at once! I’ll let you be the judge of that…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Chelsea's website: https://cmparlettpelleriti.github.io/index.htmlChelsea on Twitter: https://twitter.com/ChelseaParlettMichael Betancourt's sparsity case study: https://betanalpha.github.io/assets/case_studies/modeling_sparsity.htmlLBS #31 -- Bayesian Cognitive Modeling & Decision-Making, with Michael Lee: https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-leeProjection predictive variable selection R package: https://mc-stan.org/projpred/SelectiveInference R package: https://cran.r-project.org/web/packages/selectiveInference/selectiveInference.pdfStatistical learning and selective inference: https://www.pnas.org/content/112/25/7629LBS #29 -- Model Assessment, Non-Parametric Models, with Aki Vehtari:

As a podcaster, I discovered that there are guests for which the hardest is to know when to stop the conversation. They could talk for hours and that would make for at least 10 fantastic episodes. Frank Harrell is one of those guests. To me, our conversation was both fascinating — thanks to Frank’s expertise and the width and depth of topics we touched on — and frustrating — I still had a gazillion questions for him!But rest assured, we talked about intent to treat and randomization, proportional odds, clinical trial design, bio stats and covid19, and even which mistakes you should do to learn Bayes stats — yes, you heard right, which mistakes. Anyway, I can’t tell you everything here — you’ll just have to listen to the episode!A long time Bayesian, Frank is a Professor of Biostatistics in the School of Medicine at Vanderbilt University. His numerous research interests include predictive models and model validation, Bayesian clinical trial design and Bayesian models, drug development, and clinical research.He holds a PhD in biostatistics from the University of North Carolina, and did his Bachelor in mathematics at the University of Alabama in Birmingham.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Frank's website and courses: https://hbiostat.org/Frank's blog: https://www.fharrell.com/Frank on Twitter: https://twitter.com/f2harrellCOVID-19 Randomized Clinical Trial Design: https://hbiostat.org/proj/covid19/Frank on GitHub: https://github.com/harrelfeRegression Modeling Strategies repository: https://github.com/harrelfe/rmsBiostatistics for Biomedical Research repository: https://github.com/harrelfe/bbrBayesian Approaches to Randomized Trials, Spiegelhalter et al.: http://hbiostat.org/papers/Bayes/spi94bay.pdfStatistical Rethinking, Richard...

Episode sponsored by Paperpile: paperpile.comGet 20% off until December 31st with promo code GOODBAYESIAN21Bonjour my dear Bayesians! Yes, it was bound to happen one day — and this day has finally come. Here is the first ever 100% French speaking ‘Learn Bayes Stats’ episode! Who is to blame, you ask? Well, who better than Rémi Louf?Rémi currently works as a senior data scientist at Ampersand, a big media marketing company in the US. He is the author and maintainer of several open source libraries, including MCX and BlackJAX. He holds a PhD in statistical Physics, a Masters in physics from the Ecole Normale Supérieure and a Masters in Philosophy from Oxford University.I think I know what you’re wondering: how the hell do you go from physics to philosophy to Bayesian stats?? Glad you asked, as it was my first question to Rémi! He’ll also tell us why he created MXC and BlackJax, what his main challenges are when working on open-source projects, and what the future of PPLs looks like to him.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Rémi on GitHub: https://github.com/rloufRémi on Twitter: https://twitter.com/remiloufRémi's website: https://rlouf.github.io/BlackJAX -- Fast & modular sampling library: https://github.com/blackjax-devs/blackjaxMCX -- Probabilistic programs on CPU & GPU, powered by JAX: https://github.com/rlouf/mcxaeppl, Tools for a PPL in Aesara: https://github.com/aesara-devs/aepplFrench Presidents' popularity dashboard: https://www.pollsposition.com/popularityHow to model presidential approval (in French):

Episode sponsored by Paperpile: paperpile.comGet 20% off until December 31st with promo code GOODBAYESIAN21I don’t know if you’ve heard, but there is a virus that took over most of the world in the past year? I haven’t dedicated any episode to Covid yet. First because research was moving a lot — and fast. And second because modeling Covid is very, very hard.But we know more about it now, so I thought it was a good time to pause and ponder — how does the virus circulate? How can we model it and, ultimately, defeat it? What are the challenges in doing so?To talk about that, I had the chance to host Michael Osthege and Thomas Vladeck, who both were part of the team who developed the Rt-live model, a Bayesian model to infer the reproductive rate of Covid19 in the general population. As you’ll hear, modeling the evolution of this virus is challenging, fascinating, and a perfect fit for Bayesian modeling! It truly is a wonderful example of Bayesian generative modeling.Tom is the Managing Director of Gradient Metrics, a quantitative market research firm, and a Co-Founder of Recast, a media mix model for modern brands.Michael is a PhD student in laboratory automation and bioprocess optimization at the Forschungszentrum Jülich in Germany, and a fellow PyMC core-developer. As he works a lot on the coming brand new version 4, we’ll take this opportunity to talk about the current developments and where the project is headed.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Tom on Twitter: https://twitter.com/tvladeckTom's newsletter: https://tvladeck.substack.com/Michael on Twitter: https://twitter.com/theCakeMichael on GitHub: https://github.com/michaelosthegeRt Live dashboard: https://rtlive.de/global.htmlRt Live model tutorial:

Episode sponsored by Paperpile: paperpile.comGet 20% off until December 31st with promo code GOODBAYESIAN21We often talk about applying Bayesian statistics on this podcast. But how do we teach them? What’s the best way to introduce them from a young age and make sure the skills students learn in the stats class are transferable?Well, lucky us, Mine Dogucu’s research tackles precisely those topics!An Assistant Professor of Teaching in the Department of Statistics at University of California Irvine, Mine is both an educator with an interest in statistics, and an applied statistician with experience in educational research.Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education more accessible. In particular, she teaches an undergraduate Bayesian course, and is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R.In other words, Mine is not only interested in teaching, but also in how best to teach statistics – how to engage students in remote classes, how to get to know them, how to best record and edit remote courses, etc. She writes about these topics on her blog, DataPedagogy.com.She also works on accessibility and inclusion, as well as a study that investigates how popular Bayesian courses are at the undergraduate level in the US — that should be fun to talk about!Mine did her Master’s at Bogazici University in Istanbul, Turkey, and then her PhD in Quantitative Research, Evaluation, and Measurement at Ohio State University.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, John Johnson, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode and Patrick Kelley.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Mine's website: https://mdogucu.ics.uci.edu/index.htmlMine's blog: https://www.datapedagogy.com/Mine on Twitter: https://twitter.com/MineDogucuMine on GitHub: https://github.com/mdogucuBayes Rules! An Introduction to Bayesian Modeling with R: https://www.bayesrulesbook.com/R package for Supplemental Materials for the Bayes Rules! Book:

Let’s think Bayes, shall we? And who better to do that than the author of the well known book, Think Bayes — Allen Downey himself! Since the second edition was just released, the timing couldn’t be better!Allen is a professor at Olin College and the author of books related to software and data science, including Think Python, Think Bayes, and Think Complexity. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. from U.C. Berkeley, and bachelors and masters degrees from MIT.In this special episode, Allen and I talked about his background, how he came to the stats and teaching worlds, and why he wanted to write this book in the first place. He’ll tell us who this book is written for, what’s new in the second edition, and which mistakes his students most commonly make when starting to learn Bayesian stats. We also talked about some types of models, their usefulness and their weaknesses, but I’ll let you discover that.Now for another good news: 5 Patrons of the show will get Think Bayes for free! To qualify, you just need to go the form I linked to in the 'Learn Bayes Stats' Slack channel or the Patreon page and enter your email address. That’s it. After a week or so, Allen and I will choose 5 winners at random, who will receive the book for free!If you’re not a Patron yet, make sure to check out patreon.com/learnbayesstats if you don’t want to miss out on these goodies!And even if you’re not a Patron, I love you dear listeners, so you all get a discount when you go buy the book at https://www.learnbayesstats.com/buy-think-bayes (unfortunately, this only applies for purchases in the US and Canada).Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, John Johnson and Hector Munoz.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Give LBS a 5-star rating on Podchaser: https://www.podchaser.com/learnbayesstatsBuy Think Bayes at a 40% discount with the code LBS40 (expires on July 31; only applies for purchases in the US and Canada): https://www.learnbayesstats.com/buy-think-bayesThink Bayes 2 online:...

We all know about these accidental discoveries — penicillin, the heating power of microwaves, or the famous (and delicious) tarte tatin. I don’t know why, but I just love serendipity. And, as you’ll hear, this episode is deliciously full of it…Thanks to Allison Hilger and Timo Roettger, we’ll discover the world of linguistics, how Bayesian stats are helpful there, and how Paul Bürkner’s BRMS package has been instrumental in this field. To my surprise — and perhaps yours — the speech and language sciences are pretty quantitative and computational!As she recently discovered Bayesian stats, Allison will also tell us about the challenges she’s faced from advisors and reviewers during her PhD at Northwestern University, and the advice she’d have for people in the same situation.Allison is now an Assistant Professor at the University of Colorado Boulder. The overall goal in her research is to improve our understanding of motor speech control processes, in order to inform effective speech therapy treatments for improved speech naturalness and intelligibility. Allison also worked clinically as a speech-language pathologist in Chicago for a year. As a new Colorado resident, her new hobbies include hiking, skiing, and biking — and then reading or going to dog parks when she’s to tired.Holding a PhD in linguistics from the University of Cologne, Germany, Timo is an Associate Professor for linguistics at the University of Oslo, Norway. Timo tries to understand how people communicate their intentions using speech – how are speech signals retrieved; how do people learn and generalize? Timo is also committed to improving methodologies across the language sciences in light of the replication crisis, with a strong emphasis on open science.Most importantly, Timo loves hiking, watching movies or, even better, watching people play video games!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt and Andrew Moskowitz.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Allison's website: https://allisonhilger.com/Allison on Twitter: https://twitter.com/drahilgerAllison's motor speech lab: https://www.colorado.edu/lab/motor-speech/Timo's website: https://www.simplpoints.com/Timo on Twitter: https://twitter.com/TimoRoettgerBayesian...

Episode sponsored by Tidelift: tidelift.comIt’s been a while since we talked about biostatistics and bioinformatics on this podcast, so I thought it could be interesting to talk to Jacki Buros — and that was a very good idea!She’ll walk us through examples of Bayesian models she uses to, for instance, work on biomarker discovery for cancer immunotherapies. She’ll also introduce you to survival models — their usefulness, their powers and their challenges.Interestingly, all of this will highlight a handful of skills that Jacki would try to instill in her students if she had to teach Bayesian methods.The Head of Data and Analytics at Generable, a state-of-the-art Bayesian platform for oncology clinical trials, Jacki has been working in biostatistics and bioinformatics for over 15 years. She started in cardiology research at the TIMI Study Group at Harvard Medical School before working in Alzheimer’s Disease genetics at Boston University and in biomarker discovery for cancer immunotherapies at the Hammer Lab. Most recently she was the Lead Biostatistician at the Institute for Next Generation Health Care at Mount Sinai.An open-source enthusiast, Jacki is also a contributor to Stan and rstanarm, and the author of the survivalstan package, a library of Stan models for survival analysis.Last but not least, Jacki is an avid sailor and skier!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt and Andrew Moskowitz.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Nominate "Learn Bayes Stats" as "Best Podcast of 2021" and "Best Tech Podcast" by entering its Apple feed in this form!Jacki on Twitter: https://twitter.com/jackiburosJacki on GitHub: https://github.com/jburosJacki on Orcid: https://orcid.org/0000-0001-9588-4889survivalstan -- Survival Models in Stan: https://github.com/hammerlab/survivalstanrstanarm -- R model-fitting functions using Stan:

Episode sponsored by Tidelift: tidelift.comImagine me rapping: "Let me show you how to be a good Bayesian. Change your predictions after taking information in, and if you’re thinking I’ll be less than amazing, let’s adjust those expectations!"What?? Nah, you’re right, I’m not as good as Baba Brinkman. Actually, the best to perform « Good Bayesian » live on the podcast would just be to invite him for an episode… Wait, isn’t that what I did???Well indeed! For this episode, I had the great pleasure of hosting rap artist, science communicator and revered author of « Good Bayesian », Baba Brinkman!We talked about his passion for oral poetry, his rap career, what being a good rapper means and the difficulties he encounters to establish himself as a proper rapper.Baba began his rap career in 1998, freestyling and writing songs in his hometown of Vancouver, Canada.In 2000 he started adapting Chaucer’s Canterbury Tales into original rap compositions, and in 2004 he premiered a one man show based on his Master’s thesis, The Rap Canterbury Tales, exploring parallels between hip-hop music and medieval poetry.Over the years, Baba went on to create “Rap Guides” dedicated to scientific topics, like evolution, consciousness, medicine, religion, and climate change – and I encourage you to give them all a listen!By the way, do you know the common point between rap and evolutionary biology? Well, you’ll have to tune in for the answer… And make sure you listen until the end: Baba has a very, very nice surprise for you!A little tip: if you wanna enjoy it to the fullest, I put the unedited video version of this interview in the show notes ;) By the way, let me know if you like these video live streams — I might just do them again if you do!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski and Tim Radtke.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Video live-stream of the episode: https://www.youtube.com/watch?v=YkFXpP_SvHkBaba on Twitter: https://twitter.com/bababrinkmanBaba on YouTube: https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9gBaba on Spotify:

Episode sponsored by Tidelift: tidelift.comI don’t know about you, but the notion of time is really intriguing to me: it’s a purely artificial notion; we humans invented it — as an experiment, I asked my cat what time it was one day; needless to say it wasn’t very conclusive… And yet, the notion of time is so central to our lives — our work, leisures and projects depend on it.So much so that time series predictions represent a big part of the statistics and machine learning world. And to talk about all that, who better than a time master, namely Sean Taylor?Sean is a co-creator of the Prophet time series package, available in R and Python. He’s a social scientist and statistician specialized in methods for solving causal inference and business decision problems. Sean is particularly interested in building tools for practitioners working on real-world problems, and likes to hang out with people from many fields — computer scientists, economists, political scientists, statisticians, machine learning researchers, business school scholars — although I guess he does that remotely these days…Currently head of the Rideshare Labs team at Lyft, Sean was a research scientist and manager on Facebook’s Core Data Science Team and did a PhD in information systems at NYU’s Stern School of Business. He did his undergraduate at the University of Pennsylvania, studying economics, finance, and information systems. Last but not least, he grew up in Philadelphia, so, of course, he’s a huge Eagles fan! For my non US listeners, we’re talking about the football team here, not the bird!We also talked about two of my favorite topics — science communication and epistemology — so I had a lot of fun talking with Sean, and I hope you’ll deem this episode a good investment of your time Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Sean's website: https://seanjtaylor.com/Sean on GitHub: https://github.com/seanjtaylorSean on Twitter: https://twitter.com/seanjtaylorProphet docs: https://facebook.github.io/prophet/Forecasting at Scale -- How and why we developed Prophet for forecasting at Facebook:

Episode sponsored by Tidelift: tidelift.comI bet you already heard of Bayesian nonparametric models, at least on this very podcast. We already talked about Dirichlet Processes with Karin Knudson on episode 4, and then about Gaussian Processes with Elizaveta Semenova on episode 21. Now we’re gonna dive into the mathematical properties of these objects, to understand them better — because, as you may know, Bayesian nonparametrics are quite powerful but also very hard to fit!Along the way, you’ll learn about probabilistic circuits, sum-product networks and — what a delight — you’ll hear from the Julia community! Indeed, my guest for this episode is no other than… Martin Trapp!Martin is a core developer of Turing.jl, an open-source framework for probabilistic programming in Julia, and a post-doc in probabilistic machine learning at Aalto University, Finland.Martin loves working on sum-product networks and Bayesian non-parametrics. And indeed, his research interests focus on probabilistic models that exploit structural properties to allow efficient and exact computations while maintaining the capability to model complex relationships in data. In other words, Martin’s research is focused on tractable probabilistic models.Martin did his MsC in computational intelligence at the Vienna University of Technology and just finished his PhD in machine learning at the Graz University of Technology. He doesn’t only like to study the tractability of probabilistic models — he also is very found of climbing!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Martin's website: https://trappmartin.github.io/Martin on GitHub: https://github.com/trappmartinMartin on Twitter: https://twitter.com/martin_trappTuring, Bayesian inference with Julia: https://turing.ml/dev/Hierarchical Dirichlet Processes: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdfThe Automatic Statistician:

Episode sponsored by Tidelift: tidelift.comOne of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul on the show and, well, that was a good call: we had an amazing conversation, spanning so many topics that I can’t list them all here!I asked him why he created BRMS, in which fields it’s mostly used, what its weaknesses are, and which improvements to the package he’s currently working on. But that’s not it! Paul also gave his advice to people realizing that Bayesian methods would be useful to their research, but who fear facing challenges from advisors or reviewers.Besides being a Bayesian rockstar, Paul is a statistician working as an Independent Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart, Germany. Previously, he has studied Psychology and Mathematics at the Universities of Münster and Hagen and did his PhD in Münster about optimal design and Bayesian data analysis, and he also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University, Finland.So, of course, I asked him about the software-assisted Bayesian workflow that he’s currently working on with Aki Vehtari, which led us to no less than the future of probabilistic programming languages…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen and Jonathan Sedar.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Paul's website: https://paul-buerkner.github.io/about/Paul on Twitter: https://twitter.com/paulbuerknerPaul on GitHub: https://github.com/paul-buerknerBRMS docs: https://paul-buerkner.github.io/brms/Stan docs: https://mc-stan.org/Bayesian workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf

Episode sponsored by Tidelift: tidelift.comWe already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we?To that end, I had the delight to talk with Lauren Kennedy! Lauren is a lecturer in Business Analytics at Monash University in Melbourne, Australia, where she develops new statistical methods to analyze social science data. Working mainly with R and Stan, Lauren studies non-representative data, multilevel modeling, post-stratification, causal inference, and, more generally, how to make inferences from the social sciences.Needless to say that I asked her everything I could about MRP, including how to choose priors, why her recent paper about structured priors can improve MRP, and when MRP is not useful. We also talked about missing data imputation, and how all these methods relate to causal inference in the social sciences.If you want a bit of background, Lauren did her Undergraduates in Psychological Sciences and Maths and Computer Sciences at Adelaide University, with Danielle Navarro and Andrew Perfors, and then did her PhD with the same advisors. She spent 3 years in NYC with Andrew Gelman’s Lab at Columbia University, and then moved back to Melbourne in 2020. Most importantly, Lauren is an adept of crochet — she’s already on her third blanket!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Lauren's website: https://jazzystats.com/Lauren on Twitter: https://twitter.com/jazzystatsLauren on GitHub: https://github.com/lauken13Improving multilevel regression and poststratification with structured priors: https://arxiv.org/abs/1908.06716Using model-based regression and poststratification to generalize findings beyond the observed sample: https://arxiv.org/abs/1906.11323Lauren's beginners Bayes workshop: https://github.com/lauken13/Beginners_Bayes_WorkshopMRP in RStanarm:

How do people choose their career? How do they change jobs? How do they even change careers? These are important questions that we don’t have great answers to. But structured data about the dynamics of labor markets are starting to emerge, and that’s what Ben Zweig is modeling at Revelio Labs.An economist and data scientist, Ben is indeed the CEO of Revelio Labs, a data science company analyzing raw labor data contained in resumes, online profiles and job postings. In this episode, he’ll tell us about the Bayesian structural time series model they built to estimate inflows and outflows from companies, using LinkedIn data — a very challenging but fascinating endeavor, as you’ll hear!As a lot of people, Ben has always used more traditional statistical models but had been intrigued by Bayesian methods for a long time. When they started working on this Bayesian time series model though, he had to learn a bunch of new methods really quickly. I think you’ll find interesting to hear how it went…Ben also teaches data science and econometrics at the NYU Stern school of business, so he’ll reflect on his experience teaching Bayesian methods to economics students. Prior to that, Ben did a PhD in economics at the City University of New York, and has done research in occupational transformation and social mobility.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Ben's bio: https://www.stern.nyu.edu/faculty/bio/benjamin-zweigRevelio Labs blog: https://www.reveliolabs.com/blog/Predicting the Present with Bayesian Structural Time Series: https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdfA Hierarchical Framework for CorrectingUnder-Reporting in Count Data: https://arxiv.org/pdf/1809.00544.pdfTensorFlow Probability module for Bayesian structural time series models: https://www.tensorflow.org/probability/api_docs/python/tfp/sts/ Fitting Bayesian structural time series with the bsts R package:

When explaining Bayesian statistics to people who don’t know anything about stats, I often say that MCMC is about walking many different paths in lots of parallel universes, and then counting what happened in all these universes.And in a sense, this whole podcast is dedicated to sampling the whole distribution of Bayesian practitioners. So, for this episode, I thought we’d take a break of pure, hard modeling and talk about how to get involved into Bayesian statistics and open-source development, how companies use Bayesian tools, and what common struggles and misperceptions the latter suffer from.Quite the program, right? The good news is that Peadar Coyle, my guest for this episode, has done all of that! Coming to us from Armagh, Ireland, Peadar is a fellow PyMC core developer and was a data science and data engineer consultant until recently – a period during which he has covered all of modern startup data science, from AB testing to dashboards to data engineering to putting models into production.From these experiences, Peadar has written a book consisting of numerous interviews with data scientists throughout the world – and do consider buying it, as money goes to the NumFOCUS organization, under which many Bayesian stats packages live, like Stan, ArviZ, PyMC, etc.Now living in London, Peadar recently founded the start-up Aflorithmic, an AI solution that aims at developing personalized voice-first solutions for brands and enterprises. Their technology is also used to support children, families and elderly coping with the mental health challenges of COVID-19 confinements.Before all that, Peadar studied physics, philosophy and mathematics at the universities of Bristol and Luxembourg. When he’s away from keyboard, he enjoys the outdoors, cooking and, of course, watching rugby!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll and Nathaniel Burbank.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:"Matchmaking Dinner" announcement: https://twitter.com/alex_andorra/status/1351136756087734272How to get acces to "Matchmaking Dinner" episodes: https://www.patreon.com/learnbayesstatsPeadar on Twitter: https://twitter.com/springcoilPeadar's website: https://peadarcoyle.com/Peadar on GitHub: https://github.com/springcoilInterviews with Data Scientists -- A discussion of the Industy and the current trends:

I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. Understanding how and why people make decisions when they don’t have all the facts is fascinating to me. That’s why I like electoral forecasting and I love cognitive sciences.So, for the first episode of 2021, I have a special treat: I had the great pleasure of hosting Michael Lee on the podcast! Yes, the Michael Lee who co-authored the book Bayesian Cognitive Modeling with Eric-Jan Wagenmakers in 2013 — by the way, the book was ported to PyMC3, I put the link in the show notes ;)This book was inspired from Michael’s work as a professor of cognitive sciences at University of California, Irvine. He works a lot on representation, memory, learning, and decision making, with a special focus on individual differences and collective cognition.Using naturally occurring behavioral data, he builds probabilistic generative models to try and answer hard real-world questions: how does memory impairment work (that’s modeled with multinomial processing trees)? How complex are simple decisions, and how do people change strategies?Echoing episode 18 with Daniel Lakens, Michael and I also talked about the reproducibility crisis: how are cognitive sciences doing, which progress was made, and what is still to do?Living now in California, Michael is originally from Australia, where he did his Bachelors of Psychology and Mathematics, and his PhD in psychology. But Michael is also found of the city of Amsterdam, which he sees as “the perfect antidote to southern California with old buildings, public transport, great bread and beer, and crappy weather”.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll and Nathaniel Burbank.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Michael's website: https://faculty.sites.uci.edu/mdlee/Michael on GitHub: https://twitter.com/mdlBayesBayesian Cognitive Modeling book: https://faculty.sites.uci.edu/mdlee/bgm/Bayesian Cognitive Modeling in PyMC3: https://github.com/pymc-devs/resources/tree/master/BCMAn application of multinomial processing tree models and Bayesian methods to understanding memory impairment: https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/viewUnderstanding the Complexity of Simple...

It’s funny how powerful symbols are, right? The Eiffel Tower makes you think of Paris, the Statue of Liberty is New-York, and the Trevi Fountain… is Rome of course! Just with one symbol, you can invoke multiple concepts and ideas.You probably know that symbols are omnipresent in mathematics — but did you know that they are also very important in statistics, especially probabilistic programming?Rest assured, I didn’t really know either… until I talked with Brandon Willard! Brandon is indeed a big proponent of relational programming and symbolic computation, and he often promotes their use in research and industry. Actually, a few weeks after our recording, Brandon started spearheading the revival of Theano through the JAX backend that we’re currently working on for the future version of PyMC3!As you guessed it, Brandon is a core developer of PyMC, and also a contributor to Airflow and IPython, just to name a few. His interests revolve around the means and methods of mathematical modeling and its automation. In a nutshell, he’s a Bayesian statistician: he likes to use the language and logic of probability to quantify uncertainty and frame problems.After a Bachelor’s in physics and mathematics, Brandon got a Master’s degree in statistics from the University of Chicago. He’s worked in different areas in his career – from finance, transportation and energy to start-ups, gov-tech and academia. Brandon particularly loves projects where popular statistical libraries are inadequate, where sophisticated models must be combined in non-trivial ways, or when you have to deal with high-dimensional and discrete processes.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho and Colin Carroll.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Brandon's website: https://brandonwillard.github.io/Brandon on GitHub: https://github.com/brandonwillardThe Future of PyMC3, or "Theano is Dead, Long Live Theano": https://pymc-devs.medium.com/the-future-of-pymc3-or-theano-is-dead-long-live-theano-d8005f8a0e9bNew Theano-PyMC library: https://github.com/pymc-devs/Theano-PyMCSymbolic PyMC: https://pymc-devs.github.io/symbolic-pymc/A Role for Symbolic Computation in the General Estimation of Statistical Models:

I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we?So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their latest book, « Regression and other stories ». He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan.An enthusiast of open-source software, Aki is a core-contributor to the ArviZ package and has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference.His numerous research interests are Bayesian probability theory and methodology, especially model assessment and selection, non-parametric models (such as Gaussian processes), feature selection, dynamic models, and hierarchical models.We talked about all that — and more — on this episode, in the context of his teaching at Aalto and the software-assisted Bayesian workflow he’s currently working on with a group of researchers.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho and Colin Carroll.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:New podcast website: https://www.learnbayesstats.com/Rate LBS on Podchaser: https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588Aki's website: https://users.aalto.fi/~ave/Aki's educational material: https://avehtari.github.io/Aki on GitHub: https://github.com/avehtariAki on Twitter: https://twitter.com/avehtariBayesian Data Analysis, 3rd edition: https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955Bayesian Data Analysis course material:

In times of crisis, designing an efficient policy response is paramount. In case of natural disasters or pandemics, it can even determine the difference between life and death for a substantial number of people. But precisely, how do you design such policy responses, making sure that risks are optimally shared, people feel safe enough to reveal necessary information, and stakeholders commit to the policies?That’s where a field of economics, industrial organization (IO), can help, as Shosh Vasserman will tell us in this episode. Shosh is an assistant professor of economics at the Stanford Graduate School of Business. Specialized in industrial organization, her interests span a number of policy settings, such as public procurement, pharmaceutical pricing and auto-insurance.Her work leverages theory, empirics and modern computation (including the Stan software!) to better understand the equilibrium implications of policies and proposals involving information revelation, risk sharing and commitment. In short, Shoshana uses theory and data to study how risk, commitment and information flows interplay with policy design. And she does a lot of this with… Bayesian models! Who said Bayes had no place in economics?Prior to Stanford, Shoshana did her Bachelor’s in mathematics and economics at MIT, and then her PhD in economics at Harvard University.This was a fascinating conversation where I learned a lot about Bayesian inference on large scale random utility logit models, socioeconomic network heterogeneity and pandemic policy response — and I’m sure you will too!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Shosh's website: https://shoshanavasserman.com/Shosh on Twitter: https://twitter.com/shoshievassHow do different reopening strategies balance health and employment: https://reopenmappingproject.com/Aggregate random coefficients logit—a generative approach: http://modernstatisticalworkflow.blogspot.com/2017/03/aggregate-random-coefficients-logita.htmlVoluntary Disclosure and Personalized Pricing: https://shoshanavasserman.com/files/2020/08/Voluntary-Disclosure-and-Personalized-Pricing.pdfSocioeconomic Network Heterogeneity and Pandemic Policy Response:

In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States?But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media events? How do you model covariation between States? How do you even communicate the model’s results and afterwards assess its performance? To talk about all this, I had the pleasure to talk to Andrew Gelman and Merlin Heidemanns.Andrew was already on episode 20, to talk about his recent book with Jennifer Hill and Aki Vehtari, “Regression and Other Stories”. He’s a professor of statistics and political science at Columbia University and works on a lot of topics, including: why campaign polls are so variable while elections are so predictable, the statistical challenges of estimating small effects, and methods for surveys and experimental design.Merlin is a PhD student in Political Science at Columbia University, and he specializes in political methodology. Prior to his PhD, he did a Bachelor's in Political Science at the Freie Universität Berlin.I hope you’ll enjoy this episode where we dove into the Bayesian model they helped develop for The Economist, and talked more generally about how to forecast elections with statistical methods, and even about the incentives the forecasting industry has as a whole.Thank you to my Patrons for making this episode possible! Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Andrew's website: http://www.stat.columbia.edu/~gelman/Andrew's blog: https://statmodeling.stat.columbia.edu/Andrew on Twitter: https://twitter.com/statmodelingMerlin's website: https://merlinheidemanns.github.io/website/Merlin on Twitter: https://twitter.com/MHeidemannsThe Economist POTUS forecast: https://projects.economist.com/us-2020-forecast/presidentHow The Economist presidential forecast works: https://projects.economist.com/us-2020-forecast/president/how-this-worksGitHub repo of the Economist model: https://github.com/TheEconomist/us-potus-modelInformation, incentives, and goals in election forecasts (Gelman, Hullman & Wlezien): http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdfHow to think about extremely...

I don’t know about you, but I’m starting to really miss traveling and just talking to people without having to think about masks, social distance and activating the covid tracking app on my phone. In the coming days, there is one event that, granted, won’t make all of that disappear, but will remind me how enriching it is to meet new people — this event is PyMCon, the first-ever conference about the PyMC ecosystem! To talk about the conference format, goals and program, I had the pleasure to host Ravin Kumar and Quan Nguyen on the show.Quan is a PhD student in computer science at Washington University in St Louis, USA, researching Bayesian machine learning and one of the PyMCon program committee chairs. He is also the author of several programming books on Python and scientific computing.Ravin is a core contributor to Arviz and PyMC, and is leading the PyMCon conference. He holds a Bachelors in Mechanical Engineering and a Masters in Manufacturing Engineering. As a Principal Data Scientist he has used Bayesian Statistics to characterize and aid decision making at organizations like SpaceX and Sweetgreen. Ravin is also currently co-authoring a book with Ari Hartikainen, Osvaldo Martin, and Junpeng Lao on Bayesian Statistics due for release in February.We talked about why they became involved in the conference, parsed through the numerous, amazing talks that are planned, and detailed who the keynote speakers will be… So, If you’re interested the link to register is in the show notes, and there are even two ways to get a free ticket: either by applying to a diversity scholarship, or by being a community partner, which is anyone or any organization working towards diversity and inclusion in tech — all links are in the show notes.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:PyMCon speakers: https://pymc-devs.github.io/pymcon/speakersRegister to PyMCon: https://www.eventbrite.com/e/pymcon-2020-tickets-121404065829PyMCon Diversity Scholarship: https://bit.ly/2J3Vb9dPyMCon Community Partner Form: https://bit.ly/35yq90LPyMC3 -- Probabilistic Programming in Python: https://docs.pymc.ioDonate to PyMC3: https://numfocus.org/donate-to-pymc3PyMC3 for enterprise: https://bit.ly/3jo9jq9Ravin on Twitter: https://twitter.com/canyon289Quan on the web: https://krisnguyen135.github.io/Quan's author page: https://amzn.to/37JsB7rAlex talks about polls on the "Local Maximum" podcast: https://bit.ly/3e1Ro7OSupport "Learning Bayesian Statistics" on Patreon: https://www.patreon.com/learnbayesstatsThank you to my Patrons for making...

Have you watched the series « The English Game », on Netflix? Well, I think you should — it’s a fascinating dive into how football went from an aristocratic to a popular sport in the late 19th century England. Today it is so popular that it became a valuable business to do statistics on the game and its players!To talk about that, I invited Kevin Minkus on the show — he’s a data scientist and soccer fan living in Philadelphia. Kevin’s currently working at Monetate on ecommerce problems, and prior to Monetate he worked on property and casualty insurance pricing.He spends a lot of his spare time working on problems in football analytics and is a contributor at American Soccer Analysis, a website and podcast dedicated to… football made or played in the US (or “soccer”, as they say over there). Kevin is responsible for some of their data management and devops, and he recently wrote a guide to football analytics for the Major League Soccer’s website, entitled « Soccer Analytics 101 ».To be honest, I had a great time talking for one hour about two of my passions — football and stats! Soooo, maybe 2020 isn’t that bad after all… Ow, and beyond football, Kevin is also into the digital humanities, web development, 3D animation, machine learning, and… the bassoon!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Kevin on Twitter: https://twitter.com/kevinminkusKevin on GitHub: https://github.com/kcm30Soccer Analytics 101: https://www.mlssoccer.com/soccer-analytics-guide/2020/soccer-analytics-101American Soccer Analysis: https://www.americansocceranalysis.com/Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

Do you know what proteins are, what they do and why they are useful? Well, be prepared to be amazed! In this episode, Seth Axen will tell us about the fascinating world of protein structures and computational biology, and how his work of Bayesian modeler fits into that!Passionate about mathematics and statistics, Seth is finishing a PhD in bioinformatics at the Sali Lab of the University of California, San Francisco (UCSF). His research interests span the broad field of computational biology: using computer science, mathematics, and statistics to understand biological systems. His current research focuses on inferring protein structural ensembles. Open source development is also very dear to his heart, and indeed he contributes to many open source packages, especially in the Julia ecosystem. In particular, he develops and maintains ArviZ.jl, the Julia port of ArviZ, a platform-agnostic python package to visualize and diagnose your Bayesian models. Seth will tell us how he became involved in ArviZ.jl, what its strengths and weaknesses are, and how it fits into the Julia probabilistic programming landscape.Ow, and as a bonus, you’ll discover why Seth is such a fan of automatic differentiation, aka « autodiff » — I actually wanted to edit this part out but Seth strongly insisted I kept it. Just kidding of course — or, am I… ?Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Seth website: http://sethaxen.com/Seth on Twitter: https://twitter.com/sethaxenSeth on GitHub: https://github.com/sethaxenArviZ.jl -- Exploratory analysis of Bayesian models in Julia: https://arviz-devs.github.io/ArviZ.jl/dev/PyCon2020 -- Colin Carroll -- Getting started with automatic differentiation: https://www.youtube.com/watch?v=NG21KWZSiokThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

If you’ve studied at a business school, you probably didn’t attend any Bayesian stats course there. Well this isn’t like that in every business schools! Elea McDonnel Feit does integrate Bayesian methods into her teaching at the business school of Drexel University, in Philadelphia, US. Elea is an Assistant Professor of Marketing at Drexel, and in this episode she’ll tell us which methods are the most useful in marketing analytics, and why.Indeed, Elea develops data analysis methods to inform marketing decisions, such as designing new products and planning advertising campaigns. Often faced with missing, unmatched or aggregated data, she uses MCMC sampling, hierarchical models and decision theory to decipher all this.After an MS in Industrial Engineering at Lehigh University and a PhD in Marketing at the University of Michigan, Elea worked on product design at General Motors and was most recently the Executive Director of the Wharton Customer Analytics Initiative.Thanks to all these experiences, Elea loves teaching marketing analytics and Bayesian and causal inference at all levels. She even wrote the book R for Marketing Research and Analytics with Chris Chapman, at Springer Press.In summary, I think you’ll be pretty surprised by how Bayesian the world of marketing is…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Elea's website: http://eleafeit.com/R for Marketing Research and Analytics: http://r-marketing.r-forge.r-project.org/Elea's Tutorials & Online Courses: http://eleafeit.com/teaching/Elea on Twitter: https://twitter.com/eleafeitElea on GitHub: https://github.com/eleafeitTutorial on Conjoint Analysis in R: https://github.com/ksvanhorn/ART-Forum-2017-Stan-TutorialTest & Roll app: https://testandroll.shinyapps.io/testandroll/Test & Roll Paper -- Profit-Maximizing A/B Tests: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3274875Principal Stratification for Advertising Experiments: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3140631CausalImpact R package: https://google.github.io/CausalImpact/CausalImpact.htmlChapter on Data Fusion in marketing: https://link.springer.com/referenceworkentry/10.1007/978-3-319-05542-8_9-1Statistical Analysis with Missing Data (Little & Rubin): https://onlinelibrary.wiley.com/doi/book/10.1002/9781119013563R-Ladies Philly YouTube channel:

If, like me, you’ve been stuck in a 40 square-meter apartment for two months, you’re going to be pretty jealous of Avi Bryant. Indeed, Avi lives on Galiano Island, Canada, not very far from Vancouver, surrounded by forest, overlooking the Salish Sea. In this natural and beautiful — although slightly deer-infested — spot, Avi runs The Gradient Retreat Center, a place where writers, makers, and code writers can take a week away from their regular lives and focus on creative work. But it’s not only to envy him that I invited Avi on the show — it’s to talk about Bayesian inference in Scala, prior elicitation, how to deploy Bayesian methods at scale, and how to enable Bayesian inference for engineers. While working at Stripe, Avi wrote Rainier, a Bayesian inference framework for Scala. Inference is based on variants of the Hamiltonian Monte Carlo sampler, and the implementation is similar to, and targets the same types of models as both Stan and PyMC3. As Avi says, depending on your background, you might think of Rainier as aspiring to be either "Stan, but on the JVM", or "TensorFlow, but for small data".In this episode, Avi will tell us how Rainier came into life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Avi on Twitter: https://twitter.com/avibryantAvi on GitHub: https://github.com/avibryantRainier -- Bayesian Inference in Scala: https://rainier.fit/The Gradient Retreat: https://gradientretreat.com/Facebook's Prophet: https://facebook.github.io/prophet/BAyesian Model-Building Interface (Bambi) in Python: https://bambinos.github.io/bambi/BRMS -- Bayesian regression models using Stan: https://paul-buerkner.github.io/brms/Using Bayesian Decision Making to Optimize Supply Chains -- Thomas Wiecki & Ravin Kumar: https://twiecki.io/blog/2019/01/14/supply_chain/Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

I bet you heard a lot about epidemiological compartmental models such as SIR in the last few months? But what are they exactly? And why are they so useful for epidemiological modeling? Elizaveta Semenova will tell you why in this episode, by walking us through the case study she recently wrote with the Stan team. She’ll also tell us how she used Gaussian Processes on spatio-temporal data, to study the spread of Malaria, or to fit dose-response curves in pharmaceutical tests. And finally, she’ll tell us how she used Bayesian neural networks for drug toxicity prediction in her latest paper, and how Bayesian neural nets behave compared to classical neural nets. Ow, and you’ll also learn an interesting link between BNNs and Gaussian Processes…I know: Liza works on _a lot_ of projects! But who is she? Well, she’s a postdoctorate in Bayesian Machine Learning at the pharmaceutical company AstraZeneca, in Cambridge, UK. Elizaveta did her masters in theoretical mathematics in Moscow, Russia, and then worked in financial services as an actuary in various European countries. She then did a PhD in epidemiology at the University of Basel, Switzerland. This is where she got interested in health applications – be it epidemiology, global health or more small-scale biological questions. But she’ll tell you all that in the episode ;)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Liza on Twitter: https://twitter.com/liza_p_semenovaLiza on GitHub: https://github.com/elizavetasemenovaLiza's blog: https://elizavetasemenova.github.io/blog/A Bayesian neural network for toxicity prediction: https://www.biorxiv.org/content/10.1101/2020.04.28.065532v2Bayesian Neural Networks for toxicity prediction -- Video presentation: https://www.youtube.com/watch?v=BCQ2oVlu_tY&t=751sBayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentation/case-studies/boarding_school_case_study.htmlAndrew Gelman's comments on the SIR case-study: https://statmodeling.stat.columbia.edu/2020/06/02/this-ones-important-bayesian-workflow-for-disease-transmission-modeling-in-stan/Determining organ weight toxicity with Bayesian causal models: https://www.biorxiv.org/content/10.1101/754853v1Material for Applied Machine Learning Days ("Embracing uncertainty"): https://github.com/elizavetasemenova/EmbracingUncertaintyPredicting Drug-Induced Liver Injury with Bayesian Machine Learning: https://pubs.acs.org/doi/abs/10.1021/acs.chemrestox.9b00264Ordered Logistic Regression in Stan, PyMC3 and Turing: https://medium.com/@liza_p_semenova/ordered-logistic-regression-and-probabilistic-programming-502d8235ad3fPyMCon website: https://pymc-devs.github.io/pymcon/PyMCon Call For Proposal: https://pymc-devs.github.io/pymcon/cfpPyMCon...

Once upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and other Stories »!As every good fairy tale, this one had its share of villains — the traps where statistical methods fall and fail you; the terrible confounders, lurking in the dark; the ill-measured data that haunt your inferences! But once you defeat these monsters, you’ll be able to think about, build and interpret regression models.This episode will be filled with stories — stories about linear regressions! Here to narrate these marvelous statistical adventures are Andrew Gelman, Jennifer Hill and Aki Vehtari — the authors of the brand new Regression and other Stories.Andrew is a professor of statistics and political science at Columbia University. Jennifer is a professor of applied statistics at NYU. She develops methods to answer causal questions related to policy research and scientific development. Aki is an associate professor in computational probabilistic modeling at Aalto University, Finland.In this episode, they tell us why they wrote this book, who it is for and they also give us their 10 tips to improve your regression modeling! We also talked about the limits of regression and about going to Mars…Other good news: until October 31st 2020, you can go to http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020 and buy the book with a 20% discount by entering the promo code “GoodBayesian2020” upon checkout!That way, you’ll make up your own stories before going to sleep and dream of a world where we can easily generalize from sample to population, and where multilevel regression with poststratification is a bliss…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Regression and Other Stories on Cambridge Press website: http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020Amazon page (because of VAT laws, in some regions ordering from Amazon can be cheaper than from the editor directly, even with the discount): https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398XCode, data and examples for the book: https://avehtari.github.io/ROS-Examples/Port of the book in Python and Bambi: https://github.com/bambinos/Bambi_resources/tree/master/ROSAndrew's home page: http://www.stat.columbia.edu/~gelman/Andrew's blog: https://statmodeling.stat.columbia.edu/Andrew on Twitter: https://twitter.com/statmodelingJennifer's home page:

Do you know Turing? Of course you do! With Soss and Gen, it’s one of the blockbusters to do probabilistic programming in Julia. And in this episode Cameron Pfiffer will tell us all about it — how it came to life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.Cameron did some Rust, some Python, but he especially loves coding in Julia. That’s also why he’s one of the core-developers of Turing.jl. He’s also a PhD student in finance at the University of Oregon and did his master’s in finance at the University of Reading. His interests are pretty broad, from cryptocurrencies, algorithmic and high-frequency trading, to AI in financial markets and anomaly detection – in a nutshell he’s a fan of topics where technology is involved.As he’s the first economist to come to the show, I also asked him how Bayesian the field of economics is, why he thinks economics is quite unique among the social sciences, and how economists think about causality — I later learned that this topic is pretty controversial!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Bayesian Econometrics on Cameron's Blog: http://cameron.pfiffer.org/2020/03/24/bayesian-econometrics/Cameron on Twitter: https://twitter.com/cameron_pfifferCameron on GitHub: https://github.com/cpfifferTuring.jl -- Bayesian inference in Julia: https://turing.ml/dev/Gen.jl -- Programmable inference embedded in Julia: https://www.gen.dev/Soss.jl -- Probabilistic programming via source rewriting: https://github.com/cscherrer/Soss.jlThe Julia Language -- A fresh approach to technical computing: https://julialang.org/What is Probabilistic Programming -- Cornell University: http://adriansampson.net/doc/ppl.htmlMostly Harmless Econometrics Book: http://www.mostlyharmlesseconometrics.com/Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say how grateful I am to be in a community that values this kind of work!The Patreon page is now live at patreon.com/learnbayesstats. It starts as low as 3€ and you can pick from 4 different tiers:"Maximum A Posteriori" (3€): Join the Slack, where you can ask questions about the show, discuss with like-minded Bayesians and meet them in-person when you travel the world."Full Posterior" (5€): Previous tier + Your name in all the show notes, and I'll express my gratitude to you in the first episode to go out after your contribution. You also get early access to the special episodes. -- that I'll make at an irregular pace and will include panel discussions, book releases, live shows, etc."Principled Bayesian" (20€): Previous tiers + Every 2 months, I'll ask my guest two questions voted-on by "Principled Bayesians". I'll probably do that with a poll in the Slack channel, which will be only answered by the "Principled Bayesians" and of these questions, I will ask the top 2 every two months on the show. "Good Bayesian" (200€, only 8 spots): Previous tiers + Every 2 months, you can come on the show and you ask one question to the guest without a vote. So that's why I can't have too many people in that tier.Before telling you the best part: I already have a lot of ideas for exclusive content and options. I first need to see whether you're as excited as I am about it. If I see you are, I'll be able to add new perks to the tiers! So give me your feedback about the current tiers or any benefits you'd like to see there... but don't see yet! BTW, you have a new way to do that now: sending me voice messages at anchor.fm/learn-bayes-stats/message!Now, the icing on the cake: until July 31st, if you choose the "Full Posterior" tier (5$) or higher, you get early access to the very special episode I'm planning with Andrew Gelman, Jennifer Hill and Aki Vehtari about their upcoming book, "Regression and other stories". To top it off, there will be a promo code in the episode to buy the book at a discount price — now, that is an offer you can't turn down!Alright, that is it for today — I hope you’re as excited as I am for this new stage in the podcast’s life! Please keep the emails, the tweets, the voice messages, the carrier pigeons coming with your feedback, questions and suggestions.In the meantime, take care and I’ll see you in the next episode — episode 19, with Cameron Pfiffer, who’s the first economist to come on the show and who’s a core-developer of Turing.jl. We’re gonna talk about the Julia probabilistic programming landscape, Bayes in economics and causality — it’s gonna be fun ;) Again, patreon.com/learnbayesstats if you want to support the show and unlock some nice perks. Thanks again, I am very grateful for any support you can bring me!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:LBS Patreon page: patreon.com/learnbayesstatsSend me voice messages:

How do you design a good experimental study? How do you even know that you’re asking a good research question? Moreover, how can you align funding and publishing incentives with the principles of an open source science?Let’s do another “big picture” episode to try and answer these questions! You know, these episodes that I want to do from time to time, with people who are not from the Bayesian world, to see what good practices there are out there. The first one, episode 15, was focused on programming and python, thanks to Michael Kennedy. In this one, you’ll meet Daniel Lakens. Daniel is an experimental psychologist at the Human-Technology Interaction group at Eindhoven University of Technology, in the Netherlands. He’s worked there since 2010, when he received his PhD in social psychology. His research focuses on how to design and interpret studies, applied meta-statistics, and reward structures in science. Daniel loves teaching about research methods and about how to ask good research questions. He even crafted free Coursera courses about these topics. A fervent advocate of open science, he prioritizes scholar articles review requests based on how much the articles adhere to Open Science principles. On his blog, he describes himself as ‘the 20% Statistician’. Why? Well, he’ll tell you in the episode…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Daniel's website: https://sites.google.com/site/lakens2/HomeThe 20% Statistician: http://daniellakens.blogspot.com/Daniel on GitHub: https://github.com/LakensDaniel on Twitter: https://twitter.com/lakensDaniel on Google Scholar: https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&hl=nlCoursera Course -- Improving your statistical inferences: https://www.coursera.org/learn/statistical-inferencesCoursera Course -- Improving Your Statistical Questions: https://www.coursera.org/learn/improving-statistical-questionsPeer Reviewers' Openness Initiative: https://opennessinitiative.org/The Scientific Paper Is Obsolete -- Here’s what’s next: https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/

Have you already encountered a model that you know is scientifically sound, but that MCMC just wouldn’t run? The model would take forever to run — if it ever ran — and you would be greeted with a lot of divergences in the end. Yeah, I know, my stress levels start raising too whenever I hear the word « divergences »…Well, you’ll be glad to hear there are tricks to make these models run, and one of these tricks is called re-parametrization — I bet you already heard about the poorly-named non-centered parametrization?Well fear no more! In this episode, Maria Gorinova will tell you all about these model re-parametrizations! Maria is a PhD student in Data Science & AI at the University of Edinburgh. Her broad interests range from programming languages and verification, to machine learning and human-computer interaction. More specifically, Maria is interested in probabilistic programming languages, and in exploring ways of applying program-analysis techniques to existing PPLs in order to improve usability of the language or efficiency of inference.As you’ll hear in the episode, she thinks a lot about the language aspect of probabilistic programming, and works on the automation of various “tricks” in probabilistic programming: automatic re-parametrization, automatic marginalization, automatic and efficient model-specific inference.As Maria also has experience with several PPLs like Stan, Edward2 and TensorFlow Probability, she’ll tell us what she thinks a good PPL design requires, and what the future of PPLs looks like to her.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Maria on the Web: http://homepages.inf.ed.ac.uk/s1207807/index.htmlMaria on Twitter: https://twitter.com/migorinovaMaria on GitHub: https://github.com/mgorinovaAutomatic Reparameterisation of Probabilistic Programs (Maria's paper with Dave Moore and Matthew Hoffman): https://arxiv.org/abs/1906.03028Stan User's Guide on Reparameterization: https://mc-stan.org/docs/2_23/stan-users-guide/reparameterization-section.htmlHMC for hierarchical models -- Background on reparameterization: https://arxiv.org/abs/1312.0906NeuTra -- Automatic reparameterization: https://arxiv.org/abs/1903.03704Edward2 -- A library for probabilistic modeling, inference, and criticism: http://edwardlib.org/Pyro -- Automatic reparameterization and marginalization: https://pyro.ai/Gen -- Programmable inference: http://probcomp.csail.mit.edu/software/gen/TensorFlow Probability: https://www.tensorflow.org/probability/

A librarian, a philosopher and a statistician walk into a bar — and they can’t find anybody to talk to; nobody seems to understand what they are talking about. Nobody? No! There is someone, and this someone is Will Kurt! Will Kurt is the author of ‘Bayesian Statistics the Fun Way’ and ‘Get Programming With Haskell’. Currently the lead Data Scientist for the pricing and recommendations team at Hopper, he also blogs about stats and probability at countbayesie.com.In this episode, he’ll tell us how a Boston librarian can become a Data Scientist and work with Bayesian models everyday. He’ll also explain the value of Bayesian inference from a philosophical standpoint, why it’s useful in the travel industry and how his latest book came into life.Finally, Will is also a big fan of the “mind projection fallacy”, an informal fallacy first described by physicist and Bayesian philosopher Edwin Thompson Jaynes. Does that intrigue you? Well, stay tuned, he’ll tell us more in the episode…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Will's Blog: https://www.countbayesie.comWill on Twitter: https://twitter.com/willkurtBayesian Statistics the Fun Way -- Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks: https://nostarch.com/learnbayesGet Programming with Haskell: https://www.amazon.com/Get-Programming-Haskell-Will-Kurt/dp/1617293768The Mind Projection Fallacy: https://en.wikipedia.org/wiki/Mind_projection_fallacyProbability Theory -- The Logic of Science by E.T. Jaynes: https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99Wittgenstein's Lectures on the Foundations of Mathematics: https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261

This is it folks! This is the first of the special episodes I want to do from time to time, to expand our perspective and get inspired by what’s going on elsewhere. The guests will not come directly from the Bayesian world, but will still be related to science or programming.For the first episode of the kind, I had the chance to chat with Michael Kennedy! Michael is not only a very knowledgeable and respected member of the Python community, he’s also the founder and host of Talk Python To Me, the most popular Python podcast. He’s the founder and chief author at Talk Python Training, where he develops many Python developer online courses. And before that, Michael was a professional software trainer for over 10 years – he has taught numerous developers throughout the world! But Michael is not only an entrepreneur and teacher – he’s also a father, a husband, and a proud inhabitant of Portland, OR! As you’ll hear, our conversation spanned a large array of topics — the role of Python in science and research; how it came to be so important in data science, and why; what are Python’s threats and weaknesses and how it should evolve to not become obsolete. Michael also has interesting thoughts on the role of programming in education and how it relates to geometry — but I’ll let you discover that one by yourself…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Michael on Twitter: https://twitter.com/mkennedyThe Talk Python Podcast: https://talkpython.fm/The Python Bytes Podcast: https://pythonbytes.fm/Michael's blog: https://blog.michaelckennedy.net/Michael on Crowdcast: https://www.crowdcast.io/mkennedyJupytext -- Turn Jupyter Notebooks to scripts and (R) Markdown files: https://jupytext.readthedocs.io/en/latest/introduction.html

I bet you love penguins, right? The same goes for koalas, or puppies! But what about sharks? Well, my next guest loves sharks — she loves them so much that she works a lot with marine biologists, even though she’s a statistician! Vianey Leos Barajas is indeed a statistician primarily working in the areas of statistical ecology, time series modeling, Bayesian inference and spatial modeling of environmental data. Vianey did her PhD in statistics at Iowa State University and is now a postdoctoral researcher at North Carolina State University.In this episode, she’ll tell us what she’s working on that involves sharks, sheep and other animals! Trying to model animal movements, Vianey often encounters the dreaded multimodal posteriors. She’ll explain why these can be very tricky to estimate, and why ecological data are particularly suited for hidden Markov models and spatio-temporal models — don’t worry, Vianey will explain what these models are in the episode!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Vianey on Twitter: https://twitter.com/vianey_lbHidden Markov Models in the Stan User's Guide: https://mc-stan.org/docs/2_18/stan-users-guide/hmms-section.htmlTagging Basketball Events with HMM in Stan: https://mc-stan.org/users/documentation/case-studies/bball-hmm.htmlHMMs with Python and PyMC3: https://ericmjl.github.io/bayesian-analysis-recipes/notebooks/markov-models/The Discrete Adjoint Method -- Efficient Derivatives for Functions of Discrete Sequences (Betancourt, Margossian, Leos-Barajas): https://arxiv.org/abs/2002.00326Vianey will be doing an HMM 90-minute introduction at the International Statistical Ecology Conference in June 2020: http://www.isec2020.org/Stan for Ecology -- a website for the ecology community in Stan: https://stanecology.github.io/LatinR 2020 -- 7th to 9th October 2020: https://latin-r.com/Migramar -- Science for the Conservation of Marine Migratory Species in the Eastern Pacific: http://migramar.org/hi/en/Pelagios Kakunja -- Know, educate and conserve for a sustainable sea: https://www.pelagioskakunja.org/Book recommendations:Hidden Markov Models for Time Series: https://www.routledge.com/Hidden-Markov-Models-for-Time-Series-An-Introduction-Using-R-Second-Edition/Zucchini-MacDonald-Langrock/p/book/9781482253832Handbook of Mixture Analysis:

How is Julia doing? I’m talking about the programming language, of course! What does the probabilistic programming landscape in Julia look like? What are Julia’s distinctive features, and when would it be interesting to use it?To talk about that, I invited Chad Scherrer. Chad is a Senior Research Scientist at RelationalAI, a company that uses Artificial Intelligence technologies to solve business problems.Coming from a mathematics background, Chad did his PhD at Indiana University of Bloomington and has been working in statistics and data science for a decade now. Through this experience, he’s been using and developing probabilistic programming languages – so he’s familiar with python, R, PyMC, Stan and all the blockbusters of the field. But since 2018, he’s particularly interested in Julia and developed Soss, an open-source lightweight probabilistic programming package for Julia. In this episode, he’ll tell us why he decided to create this package, and which choices he made that made Soss what it is today. But we’ll also talk about other projects in Julia, like Turing or Gen for instance.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Chad's Website: https://cscherrer.github.io/Chad on Twitter: https://twitter.com/ChadScherrerSoss Package: https://github.com/cscherrer/Soss.jlSoss Presentation at 2019 Strata NYC: https://slides.com/cscherrer/2019-09-26-strata#/Passage -- A Parallel Sampler Generator for Hierarchical Bayesian Modeling: https://bit.ly/2UTmaYBDynamic HMC in Julia: https://github.com/tpapp/DynamicHMC.jlAdvanced HMC in Julia: https://github.com/TuringLang/AdvancedHMC.jlMonte Carlo Measurements in Julia: https://github.com/baggepinnen/MonteCarloMeasurements.jlTuring.jl -- Bayesian inference with probabilistic programming: https://turing.ml/dev/Gen.jl -- Probabilistic modeling and inference in Julia: https://www.gen.dev/Etalumis -- Bringing Probabilistic Programming to Scientific Simulators at Scale: https://arxiv.org/abs/1907.03382Omega.jl -- A programming language for causal and probabilistic reasoning: http://www.zenna.org/Omega.jl/latest/JuliaLang -- The Ingredients for a Composable Programming Language: https://white.ucc.asn.au/2020/02/09/whycompositionaljulia.htmlSimpy -- Discrete event simulation for Python:

Do you know Google Summer of Code? It’s a time of year when students can contribute to open-source software by developing and adding much needed functionalities to the open-source package of their choice. And Demetri Pananos did just that.He did it in 2019 with PyMC3, for which he developed the API for ordinary differential equations. In this episode, he’ll tell us why and how he did that, what he learned from the experience, and what the strengths and weaknesses of the API are in his opinion.Demetri is a Ph.D candidate in Biostatistics at Western University, in Ontario, Canada. His research interests surround machine learning and Bayesian statistics for personalized medicine. He earned his Master’s in Applied Mathematics from The University of Waterloo and is a firm believer in open science, interdisciplinary collaboration, and reproducible research. Other than that, he loves plotting data and drinking IPA beer – well, who doesn’t?”Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Demetri on Twitter: https://twitter.com/PhDemetriDemetri on GitHub: https://github.com/DpananosDemetri's website: https://dpananos.github.io/PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/Chris Bishop, Pattern Recognition and Machine Learning: https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738Bayesian Data Analysis (Gelman, Carlin, Stern, Dunson, Vehtari, Rubin): http://www.stat.columbia.edu/~gelman/book/Parallel Plots: https://arviz-devs.github.io/arviz/generated/arviz.plot_parallel.html

I bet you already heard about hierarchical models, or multilevel models, or varying-effects models — yeah this type of models has a lot of names! Many people even turn to Bayesian tools to build _exactly_ these models. But what are they? How do you build and use a hierarchical model? What are the tricks and classical traps? And even more important: how do you _interpret_ a hierarchical model?In this episode, Thomas Wiecki will come to the rescue and explain what multilevel models are, how to build them, what their powers are… but also why you should be very careful when building them…Does the name Thomas Wiecki ring a bell? Probably because he’s the host and creator of the PyData Deep Dive Podcast, where he interviews open-source contributors from the Python and Data Science worlds! Thomas is also the VP of Data Science at Quantopian, a crowd-sourced quantitative investment firm that encourages people everywhere to write investment algorithms.Finally, Thomas is a longtime Bayesian and core-developer of PyMC3, a fantastic python package to do probabilistic programming in Python. On his blog, he publishes tutorial articles and explores new ideas such as Bayesian Deep Learning. Caring a lot about open-source software sustainability, he puts all he’s up to on his Patreon page, that you’ll find in the show notes.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Thomas’ series on Hierarchical Regression: https://twiecki.io/blog/2013/08/12/bayesian-glms-1/Non-centered Parametrization with PyMC3: https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/Using Bayesian Decision Making: https://twiecki.io/blog/2019/01/14/supply_chain/PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/Symbolic PyMC: https://pymc-devs.github.io/symbolic-pymc/PyData Deep Dive Podcast: https://pydata-podcast.comThomas on Twitter: https://twitter.com/twiecki?lang=enThomas on Patreon: https://www.patreon.com/twieckiThomas on GitHub: https://github.com/twieckiAlex’s Hierarchical Model of Elections in Paris: https://mybinder.org/v2/gh/AlexAndorra/pollsposition_models/master?urlpath=%2Fvoila%2Frender%2Fdistrict-level%2Fmunic_model_analysis.ipynb

How do you handle your MCMC samples once your Bayesian model fit properly? Which diagnostics do you check to see if there was a computational problem? And isn’t that nice when you have beautiful and reliable plots to complement your analysis and better understand your model?I know what you think: plotting can be long and complicated in these cases. Well, not with ArviZ, a platform-agnostic package to do exploratory analysis of your Bayesian models. And in this episode, Ari Hartikainen will tell you why.Ari is a data-scientist in geophysics and a researcher at the Department of Civil Engineering of Aalto University in Finland. He mainly works on geophysics, Bayesian statistics and visualization. Ari’s also a prolific open-source contributor, as he’s a core-developer of the popular Stan and ArviZ libraries. He’ll tell us how PyStan interacts with ArviZ, what he thinks ArviZ most useful features are, and which common difficulties he encounters with his models and data.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Ari on GitHub: https://github.com/ahartikainenAri on Twitter: https://twitter.com/a_hartikainenArviZ -- Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/Introductory paper of ArviZ in The Journal of Open Source Software: https://www.researchgate.net/publication/330402908_ArviZ_a_unified_library_for_exploratory_analysis_of_Bayesian_models_in_PythonStan -- Statistical Modeling Platform: https://mc-stan.org/GPflow -- Gaussian processes in TensorFlow: https://www.gpflow.org/GPy -- Gaussian processes framework in Python: https://sheffieldml.github.io/GPy/

Have you always wondered what dark matter is? Can we even see it — let alone measure it? And what would discover it imply for our understanding of the Universe?In this episode, we’ll take look at the cosmos with Maggie Lieu. She’ll tell us what research in astrophysics is made of, what model she worked on at the European Space Agency, and how Bayesian the world of space science is.Maggie Lieu did her PhD in the Astronomy & Space Department of the University of Birmingham. She’s now a Research Fellow of Machine Learning & Cosmology at the University of Nottingham and is working on projects in preparation for Euclid, a space-based telescope whose goal is to map the dark Universe and help us learn about the nature of dark matter and dark energy.In a nutshell, she tries to help us better understand the entire cosmos. Even more amazing, she uses the Stan library and applies Bayesian statistical methods to decipher her astronomical data! But Maggie is not just a Bayesian astrophysicist: she also loves photography and rock-climbing!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Maggie's Website: https://maggielieu.com/Maggie's Google Scholar Page: https://scholar.google.co.uk/citations?user=ilfwfuUAAAAJ&hl=enMaggie on Twitter: https://twitter.com/Space_MogMaggie on GitHub: https://github.com/MaggieLieuMaggie on YouTube: https://www.youtube.com/channel/UClO6TuRE6XLzbMBmQ_KY38AStan -- Statistical Modeling Platform: https://mc-stan.org/Stan's YouTube Channel: https://www.youtube.com/channel/UCwgN5srGpBH4M-Zc2cAluOA

What is it like using Bayesian tools when you’re a software engineer or computer scientist? How do you apply these tools in the online ad industry? More generally, what is Bayesian thinking, philosophically? And is it really useful in every day life? Because, well you can’t fire up MCMC each time you need to make a quick decision under uncertainty… So how do you do that in practice, when you have at most a pen and paper?In this episode, you’ll hear Max Sklar’s take on these questions. Max is a software engineer with a focus on machine learning and Bayesian inference. Now working at Foursquare’s innovation lab, he recently led the development of a causality model for Foursquare’s Ad Attribution product and taught a course on Bayesian Thinking at the Lviv Data Science Summer School.Max is also an open-source enthusiast and a fellow podcaster – he’s the host of the Local Maximum podcast, where you can hear every week about the latest trends in AI, machine learning and technology from an engineering perspective.Ow, and if you liked the movie « Her », with Joaquin Phoenix, well you’re in for a treat at the end of this episode…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Local Maximum podcast website: https://www.localmaxradio.comMax on Twitter: https://twitter.com/maxsklarBayesian linear models: https://github.com/maxsklar/BayesPy/tree/master/LinearModelsBayesian Dirichlet-Multinomial estimation: https://github.com/maxsklar/BayesPy/tree/master/DirichletEstimationBayesian Thinking for Applied Machine Learning slides: https://docs.google.com/presentation/d/1eiceuvXlsoFKoHdqjF3qXBkyht7vR0YXQPG82ady-TU/edit?usp=sharing

You can’t study psychology up until your PhD and end-up doing very mathematical and computational data science at Google right? It’s too hard of a U-turn — some would even say it’s NUTS, just because they like bad puns… Well think again, because Junpeng Lao did just that!Before doing data science at Google, Junpeng was a cognitive psychology researcher at the University of Fribourg, Switzerland. Working in Python, Matlab and occasionally in R, Junpeng is a prolific open-source contributor, particularly to the popular TensorFlow and PyMC3 libraries. He also maintains the PyMC Discourse on his free time, where he amazingly answers all kinds of various and very specific questions!In this episode, he’ll tell you what the core characteristics of TensorFlow Probability are, and when you would use TFP instead of another probabilistic programming framework, like Stan or PyMC3. He’ll also explain why PyMC4 will be based on TensorFlow Probability itself, and what future contributions he has in mind for these two amazing libraries. Finally, Junpeng will share with you his workflow for debugging a model, or just for better understanding your models.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show: Junpeng's blog: https://junpenglao.xyz/Junpeng on Twitter: https://twitter.com/junpenglaoJunpeng on GitHub: https://github.com/junpenglaoAdvanced Bayesian Modeling Tutorial: https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439Stan Devs' Prior Choice Recommendations: https://github.com/stan-dev/stan/wiki/Prior-Choice-RecommendationsPyMC Discourse: https://discourse.pymc.io/PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/Tensor Flow Probability: https://www.tensorflow.org/probability/

If you’re there, it’s probably because you’re interested in Bayesian inference, right? But don’t you feel lost sometimes when building a model? Or you ask yourself why what you’re trying to do is so damn hard… and you conclude that YOU are the problem, that YOU must be doing something wrong!Well, rest assured, as you’ll hear from Michael Betancourt himself: it’s hard for everybody! That’s why over the years he developed and tries to popularize what he calls a « principled Bayesian workflow » — in a nutshell, think about what could have generated your data; and always question default settings!With that workflow, you’ll probably feel less alone when modeling, but expect to fail often. That’s ok — as Michael says: if you don’t fail, you don’t learn!Who is Michael Betancourt you ask? He is a physicist and statistician, whose research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that help bridge the gap between statistical theory and scientific practice.Michael works a lot on differential geometry and probability theory, and he often lives in high-dimensional spaces, where he meets with a good friend of his -- Hamiltonian Monte Carlo. Then, you won’t be surprised to learn that Michael is one of the core developers of the seminal probabilistic programming language Stan.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Michael's upcoming course: https://events.eventzilla.net/e/introduction-to-bayesian-inference-with-stan-with-michael-betancourt-2138756860Michael's website (the “Writing” page collects the case studies and pedagogical material, and the “Speaking” page links to the recorded talks): https://betanalpha.github.io/Support Michael's work on Patreon: https://patreon.com/betanalphaMichael on Twitter: https://twitter.com/betanalphaMichael on GitHub: https://github.com/betanalphaStan probabilistic programming langage: https://mc-stan.org/

I have two questions for you: Are you a self-learner? Then how do you stay up to date? What should you focus on if you’re a beginner, or if you’re more advanced?And here is my second question: Are you working in biomedicine? And if you do, are you using Bayesian tools? Then how do you get your co-workers more used to posterior distributions than p-values? In other words, how do you change behaviors in a large organization?In this episode, Eric Ma will answer all these questions and even tell us his favorite modeling techniques, which problems he encountered with these models, and how he solved them. He’ll also share with us the software-engineering workflow he uses at Novartis to share his work with colleagues.Eric is a data scientist at the Novartis Institutes for Biomedical Research, where he focuses on Bayesian statistical methods to make medicines for patients. Eric is also a prolific open source developer: he led the development of pyjanitor, an API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He also contributes to PyMC3, matplotlib and bokeh.This is « Learning Bayesian Statistics », episode 5, recorded October 21, 2019.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Links from the show:Eric's website: https://ericmjl.github.io/Eric on Twitter: https://twitter.com/ericmjlBayesian analysis recipes: https://github.com/ericmjl/bayesian-analysis-recipesBayesian deep learning demystified: https://github.com/ericmjl/bayesian-deep-learning-demystifiedCausality repo: https://github.com/ericmjl/causalityPyjanitor - Convenient data cleaning routines for repetitive tasks: https://pyjanitor.readthedocs.io/PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/Panel - A high-level app and dashboarding solution for Python: https://panel.pyviz.org/Nxviz - Visualization Package for NetworkX: https://nxviz.readthedocs.io/en/latest/

What do neurodegenerative diseases, gerrymandering and ecological inference all have in common? Well, they can all be studied with Bayesian methods — and that’s exactly what Karin Knudson is doing.In this episode, Karin will share with us the vital and essential work she does to understand aspects of neurodegenerative diseases. She’ll also tell us more about computational neuroscience and Dirichlet processes — what they are, what they do, and when you should use them.Karin did her doctorate in mathematics, with a focus on compressive sensing and computational neuroscience at the University of Texas at Austin. Her doctoral work included applying hierarchical Dirichlet processes in the setting of neural data and focused on one-bit compressive sensing and spike-sorting.Formerly the chair of the math and computer science department of Phillips Academy Andover, she started a postdoc at Mass General Hospital and Harvard Medical in Fall 2019. Most importantly, rock climbing and hiking have no secrets for her!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ ! Links from the show, personally curated by Karin Knudson:Karin on Twitter: https://twitter.com/karinknudsonSpike train entropy-rate estimation using hierarchical Dirichlet process priors (Knudson and Pillow): https://pillowlab.princeton.edu/pubs/abs_Knudson_HDPentropy_NIPS13.htmlFighting Gerrymandering with PyMC3, PyCon 2018, Colin Carroll and Karin Knudson: https://www.youtube.com/watch?v=G9I5ZnkWR0AExpository resources on Dirichlet Processes: Chapter 23 of Bayesian Data Analysis (Gelman et al.) and http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdfHierarchical Dirichlet Processes (introduced the HDP and included applications in topic modeling and for working with time-series data and Hidden Markov Models): https://www.stat.berkeley.edu/~aldous/206-Exch/Papers/hierarchical_dirichlet.pdfA Sticky HDP-HMM with applications to speaker diarization (a nice example of how the HDP can be used with HMM, in this case cleverly adapted so that states have more persistence): https://arxiv.org/abs/0905.2592If you want to get deeper into the weeds and also get a sense of the history: Dirichlet Processes with Applications to Bayesian Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342871) and A Bayesian Analysis of Some Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342360)

How can you use Bayesian tools and optimize your models in industry? What are the best ways to communicate and visualize your models with non-technical and executive people? And what are the most common pitfalls?In this episode, Colin Carroll will tell us how he did all that in finance and the airline industry. He’ll also share with us what the future of probabilistic programming looks like to him.You already heard from Colin two weeks ago — so, if you didn’t catch this episode, go back in your feed’s history and enjoy the first part! As a reminder, Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/!Links from the show: Colin's blog: https://colindcarroll.com/ Gelman’s putting model in PyMC3: https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/putting_workflow.ipynb Matthew Kay’s quantile dotplots: https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md Jax, Composable transformations of Python+NumPy programs: https://github.com/google/jax NumPyro, Probabilistic programming with NumPy: https://github.com/pyro-ppl/numpyro Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/ Rainier, Bayesian inference in Scala: https://github.com/stripe/rainier---Send in a voice message: https://anchor.fm/learn-bayes-stats/message