#118 Exploring the Future of Stan, with Charles Margossian & Brian Ward
Learning Bayesian Statistics
The Evolution and Accessibility of Hamiltonian Monte Carlo in Stan
This chapter delves into the historical development of Hamiltonian Monte Carlo and its incorporation into Stan, emphasizing technological innovations that aided scientists. It also addresses the tuning challenges faced by users and the importance of diagnostics for enhancing HMC's effectiveness.
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Takeaways:
- User experience is crucial for the adoption of Stan.
- Recent innovations include adding tuples to the Stan language, new features and improved error messages.
- Tuples allow for more efficient data handling in Stan.
- Beginners often struggle with the compiled nature of Stan.
- Improving error messages is crucial for user experience.
- BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models.
- Community engagement is vital for the development of Stan.
- New samplers are being developed to enhance performance.
- The future of Stan includes more user-friendly features.
Chapters:
00:00 Introduction to the Live Episode
02:55 Meet the Stan Core Developers
05:47 Brian Ward's Journey into Bayesian Statistics
09:10 Charles Margossian's Contributions to Stan
11:49 Recent Projects and Innovations in Stan
15:07 User-Friendly Features and Enhancements
18:11 Understanding Tuples and Their Importance
21:06 Challenges for Beginners in Stan
24:08 Pedagogical Approaches to Bayesian Statistics
30:54 Optimizing Monte Carlo Estimators
32:24 Reimagining Stan's Structure
34:21 The Promise of Automatic Reparameterization
35:49 Exploring BridgeStan
40:29 The Future of Samplers in Stan
43:45 Evaluating New Algorithms
47:01 Specific Algorithms for Unique Problems
50:00 Understanding Model Performance
54:21 The Impact of Stan on Bayesian Research
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, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke and Robert Flannery.
Links from the show:
- Come see the show live at PyData NYC: https://pydata.org/nyc2024/
- LBS #90, Demystifying MCMC & Variational Inference, with Charles Margossian: https://learnbayesstats.com/episode/90-demystifying-mcmc-variational-inference-charles-margossian/
- Charles' website: https://charlesm93.github.io/
- Charles on GitHub: https://github.com/charlesm93
- Charles on LinkedIn: https://www.linkedin.com/in/charles-margossian-3428935b/
- Charles on Google Scholar: https://scholar.google.com/citations?user=nPtLsvIAAAAJ&hl=en
- Charles on Twitter: https://x.com/charlesm993
- Brian's website: https://brianward.dev/
- Brian on GitHub: https://github.com/WardBrian
- Brian on LinkedIn: https://www.linkedin.com/in/ward-brianm/
- Brian on Google Scholar: https://scholar.google.com/citations?user=bzosqW0AAAAJ&hl=en
- Brian on Twitter: https://x.com/ward_brianm
- Bob Carpenter's reflections on StanCon: https://statmodeling.stat.columbia.edu/category/bayesian-statistics/
Transcript
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