📘 With the release of our textbook "Bayesian Workflow" (avehtari.github.io/Bayesian-Wor...), I figured I'd also share the content of my graduate course on the topic at UBC. 🌎 charlesm93.github.io/stat547/ The course contains overlapping and complementary material, homeworks and reading.

posterior R package has now JOSS paper you can cite, too joss.theoj.org/papers/10.21... with @paulbuerkner.com , Jonah Gabry, @mjskay.com

Stan embedded Laplace was a long project and happy that it's finally released. We are not competing with INLA and TMB software, as they are orders of magnitude faster and scale better with data size for models that you can implement with them 1/

#FlatironCCM's Teresa Huang explores how symmetries and structure can reveal new insights into the universe, aiding discovery in everything from cosmology to the development of fusion energy. https://www.simonsfoundation.org/teresa-huang-bridging-science-and-machine-learning-at-cosmic-scales/

do mine eyes deceive me? a release date in the present year?

StanCon 2026 is this August 17-21 in Upsala, Sweden 🇸🇪 www.stancon2026.org ⏰ Abstracts for contributed talks are due Feb 25 ⏰ Abstracts for posters are due May 27 And just to be clear: Yes, StanCon is my favorite conference to attend!! Can't wait for this one!

Bayesian Workflow by Andrew Gelman, Aki Vehtari, @rmcelreath.bsky.social with @danpsimpson.bsky.social, @charlesm993.bsky.social, @yulingy.bsky.social, Lauren Kennedy, Jonah Gabry, @paulbuerkner.com, @modrakm.bsky.social, @vianeylb.bsky.social (in production, estimated copy-editing time 6 weeks)

Simons Foundation president David Spergel recently spoke to @issuesinst.bsky.social about the future of science philanthropy: https://issues.org/american-science-simons-spergel-interview/ #science #math #philanthropy

What is "workflow" and why is it important? The latest blog post by Andrew Gelman: statmodeling.stat.columbia.edu/2026/01/08/w...

🇪🇸 This week I'm attending ICSDS (International Conference on Stats & Data Science) in Sevilla, Spain. 🤝 Looking forward to connecting with colleagues, old and new! 💡On Wednesday, I'll give a talk on "Variational Inference in the Presence of Symmetry" at the 9 am session on Bayesian learning.

Check out my poster today (Thurs) at 11am--2pm session. Exhibit Hall C,D,E Poster Location: #602 "Fisher meets Feynman: score-based variational inference with a product of experts" (NeurIPS spotlight) with Robert Gower, David Blei, and Lawrence Saul @flatironinstitute.org #NeurIPS2025

Applications for the PhD and MSc programs in statistics at UBC are now open! 📆 Deadline for PhD program is December 1st 📆 Deadline for MSc program is January 5th The department covers all areas of statistics and we have a lot of momentum in Bayesian computation!

Application for a postdoc research fellowship in computational mathematics at the Flatiron Institute in New York are now open! apply.interfolio.com/173401 📆 Deadline is December 1st. 🔭 This is an excellent place to do research at the interface of ML, stats and the natural sciences.

My paper with Loucas Pillaud-Vivien and Lawrence Saul, “Variational Inference for Uncertainty Quantification: An Analysis of Trade-offs”, has been accepted for publication in the Journal of Machine Learning Research. 📃 arxiv.org/abs/2403.13748 🧵 1/

Stan v2.37 has been released! blog.mc-stan.org/2025/09/02/r...

Yes, in principle, I start at UBC Statistics today. But right now, I'm running around the Frankfurt airport to catch my flight to Vancouver .... 🏃‍♂️🧳✈️ www.stat.ubc.ca/news/charles...

Earlier this month, I taught at the summer school on "cryptography, statistics and machine learning" (mathschool.ysu.am) hosted by Yerevan State University in Armenia 🇦🇲 🙏 Thank you to the organizers for putting together such a wonderful event! I truly enjoyed interacting with the students.

New manuscript by Nawaf Bou-Rabee, Bob Carpenter, Tore Kleppe and Sifan Liu on the WALNUTS algorithm which improves of the NUTS sampler by introducing a locally adaptive step size. 📜 Paper: arxiv.org/pdf/2506.18746 💻 Code: github.com/bob-carpente...

🇸🇬 Next stop: Singapore for BayesComp'25 (bayescomp2025.sg) The organizers put together a wonderful program! I'll be: 🪑 chairing the session on "Parallel comp for MCMC" 🎙️ speaking at the session on "Advances in VI" Looking forward to meeting researchers and catching up with colleagues.

Research opportunity for a graduate student in ecology 🌳 at UBC 🇨🇦 with Lizzie Wolkovich and the Temporal Ecology lab (temporalecology.org). 📝 Apply here: temporalecology.org/joining-the-... by July 1st 2025! The abstract sounds fascinating (see attached).

🧑‍💻 Candidate release for Stan 2.37 is out: discourse.mc-stan.org/t/cmdstan-st.... Lots of exciting features to try out, including: - embedded/integrated Laplace approximation - new constrained types (e.g. sum_to_zero_matrix) - built-in constraint transformations exposed

Taking our Models Seriously (my talk at StanBio Connect, this Friday 9am) statmodeling.stat.columbia.edu/2025/05/27/t...

✨ Thank you #AISTATS for the best paper award!! 📜 arxiv.org/abs/2410.11067 💡What does VI learn and under what conditions? The answer lies in symmetry. 🤝 Honored to share this award with my co-author Lawrence Saul from @flatironinstitute.org

🇹🇭 Just arrived in Phuket, Thailand for #AISTATS 2025. 📃 I'll presenting my recent work with Lawrence Saul on Variational Inference in Location-Sacale Families: arxiv.org/abs/2410.11067 DM if you are in town and want to connect at the conference!

For the first time, Journal-to-Conference papers have entered the schedule of #AISTATS2025 🎉 To celebrate here is a bingo card: should you be among the first to meet all of our amazing Journal-to-Conference presenters (I need proof!), I’ll buy you a drink... provided that you can find me too!

📚 Putting together a reading list for #AISTATS 2025. I already found a few very good papers, and I'm curious to hear about more accepted publications.

📣 Nominations for the Stan Governing Body are open for another two weeks. Calling on all members of the Stan community to consider this role. 🔗 discourse.mc-stan.org/t/elections-... 💡 A post about my own experience: statmodeling.stat.columbia.edu/2025/03/15/e...

I'm thrilled to share that, starting this summer, I will continue my academic journey as an assistant professor of statistics at the University of British Columbia in Vancouver, Canada. Full statement here: charlesm93.github.io/files/letter...

🚀 Exciting news! We're proposing a new *Bayesian Deep Learning* section within @isba-bayesian.bsky.social . If you support this initiative, add your name to the petition and help make it happen! 🔗 Petition: tinyurl.com/527vaamz Sinead Williamson, Theo Papamarkou @vincefort.bsky.social Sara Wade 1/2

📣 Calling all members of the Stan community to action! ❕We're renewing the Stan Governing Body. This is a fantastic way to contribute to the project. The SGB co-organizes StanCon and related events, funds developers, and helps set the directions of the project. 🧵 Link in thread.

StanConnects are a series of one-day online conferences for the application of Stan to specific areas. This year, we're kicking off with Stan for biology (broadly defined), co-organized by @ericnovik.bsky.social and @vianeylb.bsky.social, on 30 May '25!! stanbio.org

Yesterday, an undergraduate asked me whether I used the same tools as 3blue1brown to animate my slides. Maybe one of the coolest compliments I've ever gotten :) ... and excellent reason to share my appreciation for this beautiful channel: www.youtube.com/@3blue1brown.

@flatironinstitute.org released recordings of the FWAM meeting, a 2-day event during which experts present introductory talks on computational and scientific methods. www.simonsfoundation.org/event/2024-f... I had the pleasure to give a talk on: "For how many iterations should we run MCMC?"

Our paper "Batch, match, and patch: low-rank approximations for score-based variational inference" is accepted to #AISTATS2025. This paper addresses score-based variational inference with Gaussians (batch and match VI) with low rank + diagonal covariances. Preprint: arxiv.org/abs/2410.22292

Our paper was accepted (oral) for #AISTATS. From the AC: "This paper provides a very elegant analysis of variational inference in location-scale families, providing much-needed insights into when and why variational methods can so often accurately obtain accurate means estimates."

Kicking off the year with a visit to the Center for AI Fundamentals at the University of Manchester 🇬🇧 Thank you for the invitation and the opportunity to exchange ideas!

🎙️ Andrew Gelman and I were interviewed by James Rogers from Metrum Research Group (metrumrg.com)! 📝 We spoke about our BAYESIAN WORKFLOW paper (arxiv.org/abs/2011.01808) and its application to pharmacometrics. 📽️ www.youtube.com/watch?v=5BNV... Thank you Metrum for the invitation!!

A poster at #NeurIPS2024 with everything I like: - Amortized inference - Pareto-smoothed importance sampling - MCMC using many chains This work provides a progressive complexification of the inference to control computation. Work by Luigi Acerbi and colleagues: openreview.net/pdf?id=pKZp2...

Come by our #NeurIPS2024 spotlight poster on EigenVI: score-based variational inference with orthogonal function expansions! Friday 7:30pm, East Exhibit Hall A-C #3900 Link: arxiv.org/abs/2410.24054

🇨🇦 Next stop: Vancouver for #NeurIPS2024! 🏃‍♂️Ready to learn, share, discuss, connect and reconnect!! 💡 My favorite part: meeting researchers and exchanging ideas, so please DM if you want to chat! 📜 Presenting our latest paper on Variational Inference

Heading to #NeurIPS2024 next week to present EigenVI: score-based variational inference with orthogonal function expansions Link: arxiv.org/abs/2410.24054 Poster: Friday 7:30pm, East Exhibit Hall A-C #3900

Applications to our Research Fellow position at @flatironinstitute.org CCM are closing soon on Dec 15! It's a great place for doing fundamental ML research with a lot of freedom, in the heart of NYC. Apply here: apply.interfolio.com/155357

Generating cat videos is nice, but what if you could tackle real scientific problems with the same methods? 🧪🌌 Introducing The Well: 16 datasets (15TB) for Machine Learning, from astrophysics to fluid dynamics and biology. 🐙: github.com/PolymathicAI... 📜: openreview.net/pdf?id=00Sx5...

Running Markov Chain Monte Carlo on Modern Hardware and Software by Sountsov, Carroll, and Hoffman, showing some new performance benchmarks. arxiv.org/abs/2411.04260