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

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

FromLearning Bayesian Statistics


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

FromLearning Bayesian Statistics

ratings:
Length:
67 minutes
Released:
Mar 12, 2021
Format:
Podcast episode

Description

Episode sponsored by Tidelift: https://tidelift.com/ (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/ (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 (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Paul's website: https://paul-buerkner.github.io/about/ (https://paul-buerkner.github.io/about/)
Paul on Twitter: https://twitter.com/paulbuerkner (https://twitter.com/paulbuerkner)
Paul on GitHub: https://github.com/paul-buerkner (https://github.com/paul-buerkner)
BRMS docs: https://paul-buerkner.github.io/brms/ (https://paul-buerkner.github.io/brms/)
Stan docs: https://mc-stan.org/ (https://mc-stan.org/)
Bayesian workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf (https://arxiv.org/pdf/2011.01808v1.pdf)



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Released:
Mar 12, 2021
Format:
Podcast episode

Titles in the series (100)

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 Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (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 https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (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 https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy