#100 Reactive Message Passing & Automated Inference in Julia, with Dmitry Bagaev
Learning Bayesian Statistics
Challenges of Developing Open-Source Software for Scientific Research
This chapter explores the hurdles encountered by open-source developers in scientific research, emphasizing the importance of adapting to evolving scientific insights, mastering mathematical concepts, and writing efficient code. The discussion also covers the significance of user-friendly software that facilitates exploration of novel insights.
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In this episode, Dmitry Bagaev discusses his work in Bayesian statistics and the development of RxInfer.jl, a reactive message passing toolbox for Bayesian inference.
Dmitry explains the concept of reactive message passing and its applications in real-time signal processing and autonomous systems. He discusses the challenges and benefits of using RxInfer.jl, including its scalability and efficiency in large probabilistic models.
Dmitry also shares insights into the trade-offs involved in Bayesian inference architecture and the role of variational inference in RxInfer.jl. Additionally, he discusses his startup Lazy Dynamics and its goal of commercializing research in Bayesian inference.
Finally, we also discuss the user-friendliness and trade-offs of different inference methods, the future developments of RxInfer, and the future of automated Bayesian inference.
Coming from a very small town in Russia called Nizhnekamsk, Dmitry currently lives in the Netherlands, where he did his PhD. Before that, he graduated from the Computational Science and Modeling department of Moscow State University.
Beyond that, Dmitry is also a drummer (you’ll see his cool drums if you’re watching on YouTube), and an adept of extreme sports, like skydiving, wakeboarding and skiing!
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, 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 Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser and Julio.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Takeaways:
- Reactive message passing is a powerful approach to Bayesian inference that allows for real-time updates and adaptivity in probabilistic models.
- RxInfer.jl is a toolbox for reactive message passing in Bayesian inference, designed to be scalable, efficient, and adaptable.
- Julia is a preferred language for RxInfer.jl due to its speed, macros, and multiple dispatch, which enable efficient and flexible implementation.
- Variational inference plays a crucial role in RxInfer.jl, allowing for trade-offs between computational complexity and accuracy in Bayesian inference.
- Lazy Dynamics is a startup focused on commercializing research in Bayesian inference, with the goal of making RxInfer.jl accessible and robust for industry applications.
Links from the show:
- LBS Physics & Astrophysics playlist: https://learnbayesstats.com/physics-astrophysics/
- LBS #51, Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton/
- Dmitry on GitHub: https://github.com/bvdmitri
- Dmitry on LinkedIn: https://www.linkedin.com/in/bvdmitri/
- RxInfer.jl, Automatic Bayesian Inference through Reactive Message Passing: https://rxinfer.ml/
- Reactive Bayes, Open source software for reactive, efficient and scalable Bayesian inference: https://github.com/ReactiveBayes
- LazyDynamics, Reactive Bayesian AI: https://lazydynamics.com/
- BIASlab, Natural Artificial Intelligence: https://biaslab.github.io/
- Dmitry's PhD dissertation: https://research.tue.nl/en/publications/reactive-probabilistic-programming-for-scalable-bayesian-inferenc
- Effortless Mastery, by Kenny Werner: https://www.amazon.com/Effortless-Mastery-Liberating-Master-Musician/dp/156224003X
- The Book of Why, by Judea Pearl: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X
- Bernoulli’s Fallacy, by Aubrey Clayton: https://www.amazon.com/Bernoullis-Fallacy-Statistical-Illogic-Science/dp/0231199945
- Software Engineering for Science: https://www.amazon.com/Software-Engineering-Science-Chapman-Computational/dp/1498743854
Transcript
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