Episode 35

#35 The Past, Present & Future of BRMS, with Paul Bürkner

Episode sponsored by Tidelift: tidelift.com

One 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.

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About the Podcast

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Learning Bayesian Statistics
A podcast on Bayesian inference - the methods, the projects and the people who make it possible!

About your host

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Alexandre ANDORRA

Hi! I'm your host, Alex Andorra. By day, I'm a Bayesian 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 awesome Python packages PyMC and ArviZ.

An always-learning statistician, I love building models and studying elections and human behavior. I also love Nutella a bit too much, but I don't like talking about it – I prefer eating it.

My goal is to make this podcast as interesting and useful to you as possible. So, hit me on Twitter or email with your questions and suggestions!