Learning Bayesian Statistics

When speaking about Bayesian statistics, we often hear about « probabilistic programming » — but what is it? Which languages and libraries allow you to program probabilistically? When is Stan, PyMC, Pyro or any other probabilistic programming language most appropriate for your project? And when should you even use Bayesian libraries instead of non-bayesian tools, like Statsmodels or Scikit-learn?Colin Carroll will answer all these questions for you. 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.Having studied geometric measure theory at Rice University, Colin was bound to walk in the woods with Pete the pup – who was there when we recorded by the way – and to launch balloons into near-space in his spare 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/!Links from the show:Colin's blog: https://colindcarroll.com/Colin on Twitter: https://twitter.com/colindcarrollColin on GitHub: https://github.com/ColCarrollVery parallel MCMC sampling: https://colindcarroll.com/2019/08/18/very-parallel-mcmc-sampling/A tour of probabilistic programming APIs: https://colindcarroll.com/2019/07/23/a-tour-of-probabilistic-programming-apis/PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/Stan: https://mc-stan.org/Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/ArviZ, Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/ PyMC-Learn, Probabilistic models for machine learning: https://www.pymc-learn.org/Facebook’s Prophet uses Stan: https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/Prophet in PyMC3: https://github.com/luke14free/pm-prophet

When are Bayesian methods most useful? Conversely, when should you NOT use them? How do you teach them? What are the most important skills to pick-up when learning Bayes? And what are the most difficult topics, the ones you should maybe save for later?In this episode, you’ll hear Chris Fonnesbeck answer these questions from the perspective of marine biology and sports analytics. Chris is indeed the New York Yankees’ senior quantitative analyst and an associate professor at Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He also created PyMC, a library to do probabilistic programming in python, and is the author of several tutorials at PyCon and PyData conferences.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:Chris on Twitter: https://twitter.com/fonnesbeckPyMC3, Probabilistic Programming in Python: https://docs.pymc.io/Chris on GitHub: https://github.com/fonnesbeckAn introduction to Markov Chain Monte Carlo using PyMC3 - PyData London 2019: https://www.youtube.com/watch?v=SS_pqgFziAgIntroduction to Statistical Modeling with Python - PyCon 2017 - video: https://www.youtube.com/watch?v=TMmSESkhRtIIntroduction to Statistical Modeling with Python - PyCon 2017 - code repo: https://github.com/fonnesbeck/intro_stat_modeling_2017Bayesian Non-parametric Models for Data Science using PyMC3 - PyCon 2018: https://www.youtube.com/watch?v=-sIOMs4MSuAStatistical Data Analysis in Python: https://github.com/fonnesbeck/statistical-analysis-python-tutorial

What do you get when you put a physicist, a biologist and a data scientist in the same body? Well, you’re about to find out… In this episode you’ll meet Osvaldo Martin. Osvaldo is a researcher at the National Scientific and Technical Research Council in Argentina and is notably the author of the book Bayesian Analysis with Python, whose second edition was published in December 2018. He also teaches bioinformatics, data science and Bayesian data analysis, and is a core developer of PyMC3 and ArviZ, and recently started contributing to Bambi. Originally a biologist and physicist, Osvaldo trained himself to python and Bayesian methods – and what he’s doing with it is pretty amazing!We also touch on how accepted are Bayesian methods in his field, which models he’s currently working on, and what it’s like to be an open-source developer.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 Analysis with Python, 2nd edition: https://www.amazon.com/dp/B07HHBCR9GBayesian Analysis with Python, code repository; https://github.com/aloctavodia/BAPOsvaldo on Twitter: https://twitter.com/aloctavodiaPyMC3, Probabilistic Programming in Python: https://docs.pymc.io/ArviZ, Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/BAyesian Model-Building Interface (BAMBI) in Python: https://bambinos.github.io/bambi/

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Well I'm just like you! When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible.So I created "Learning Bayesian Statistics", a fortnightly podcast where I interview researchers and practitioners of all fields about why and how they use Bayesian statistics, and how in turn YOU, as a learner, can apply these methods in YOUR modeling workflow. Now the thing is, I’m not a beginner, but I’m not an expert either. The people I’ll interview will definitely be. So I’ll be learning alongside you. I won’t pretend to know everything in this podcast, and I WILL make mistakes. But thanks to the guests’ feedback, we’ll be able to learn from those mistakes, and I think this will help you (and me!) become better, faster, stronger Bayesians.So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you. In this very first episode - well actually it’s episode 0, because 0-indexing rules! - I will introduce you to the genesis of this podcast, tell you why you should listen and reveal some of the guests for the coming episodes.Come join us!Links from the show:Podcast website: https://learnbayesstats.anvil.app/Alex Twitter feed: https://twitter.com/alex_andorra