#123 BART & The Future of Bayesian Tools, with Osvaldo Martin

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:BART models are non-parametric Bayesian models that approximate functions by summing trees.BART is recommended for quick modeling without extensive domain knowledge.PyMC-BART allows mixing BART models with various likelihoods and other models.Variable importance can be easily interpreted using BART models.PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.The integration of BART with Bambi could enhance exploratory modeling.Teaching Bayesian statistics involves practical problem-solving approaches.Future developments in PyMC-BART include significant speed improvements.Prior predictive distributions can aid in understanding model behavior.Interactive learning tools can enhance understanding of statistical concepts.Integrating PreliZ with PyMC improves workflow transparency.Arviz 1.0 is being completely rewritten for better usability.Prior elicitation is crucial in Bayesian modeling.Point intervals and forest plots are effective for visualizing complex data.Chapters:00:00 Introduction to Osvaldo Martin and Bayesian Statistics08:12 Exploring Bayesian Additive Regression Trees (BART)18:45 Prior Elicitation and the PreliZ Package29:56 Teaching Bayesian Statistics and Future Directions45:59 Exploring Prior Predictive Distributions52:08 Interactive Modeling with PreliZ54:06 The Evolution of ArviZ01:01:23 Advancements in ArviZ 1.001:06:20 Educational Initiatives in Bayesian Statistics01:12:33 The Future of Bayesian MethodsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin...

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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!