#6 A principled Bayesian workflow, with Michael Betancourt

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:

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Om Podcasten

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? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. 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", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!