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#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

Released Thursday, 2nd October 2025
Good episode? Give it some love!
#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

Thursday, 2nd October 2025
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!


Our 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 as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
  • Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
  • Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
  • Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
  • Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
  • Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.


Chapters:

05:10 – From economics to IoT and Bayesian statistics

18:55 – Introduction to BART (Bayesian Additive Regression Trees)

24:40 – Re-implementing BART in Rust for speed and scalability

32:05 – Comparing BART with Gaussian Processes and other tree methods

39:50 – Strengths and limitations of BART

47:15 – Handling missing data and different likelihoods

54:30 – Variational inference and big data challenges

01:01:10 – Embedding BART into optimization and decision-making frameworks

01:08:45 – Open source, PyMC, and community support

01:15:20 – Advice for newcomers

01:20:55 – Future of BART, Rust, and probabilistic programming

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, 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...

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From The Podcast

Learning Bayesian Statistics

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. By day, I'm a Senior data scientist. 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 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!

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