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Statistical Rethinking 2026 Lecture B04 - Group-level confounding and intro to social networks

February 4, 2026 at 7:15 PM

Reposted by Aki Vehtari

Riccardo Fusaroli

@fusaroli.bsky.social

I also want to hear about your favorite recent (<5 years 😅) Stan and Stan-adjacent developments that changed your bayesian framework (thinking about models, validating models, fitting models and checking their fit and how they break)!

Riccardo Fusaroli @fusaroli.bsky.social · 7h

as I'm revising my course materials, I keep stumbling upon cool @mc-stan.org developments.

Current favorites:
1. your model has funnels and you exhausted reparametrization ideas: metric = "dense_e" makes your HMC learn about covariance btw parameters. Sloooow, but effective!
1/

February 4, 2026 at 1:35 PM

Reposted by Aki Vehtari

Riccardo Fusaroli

@fusaroli.bsky.social

as I'm revising my course materials, I keep stumbling upon cool @mc-stan.org developments.

Current favorites:
1. your model has funnels and you exhausted reparametrization ideas: metric = "dense_e" makes your HMC learn about covariance btw parameters. Sloooow, but effective!
1/

February 4, 2026 at 1:30 PM

Reposted by Aki Vehtari

Sam Abbott

@seabbs.bsky.social

New pre-pre-print:

February 3, 2026 at 7:29 PM

Reposted by Aki Vehtari

Richard McElreath 🐈‍⬛

@rmcelreath.bsky.social

This time I try to explain group-level confounding and some ways to deal with it. Lecture B04 of Statistical Rethinking 2026 - fixed effects, Mundlak machines, latent Mundlak machines, intro to social network analysis and the social relations model. Full lecture list: github.com/rmcelreath/s...

Chapter 2 The Pizza Experiment | Advanced Cognitive Modeling Notes

January 30, 2026 at 1:27 PM

Reposted by Aki Vehtari

Riccardo Fusaroli

@fusaroli.bsky.social

wanna learn about cognitive modeling and love pizza? I am revising my cognitive modeling course notes and could use some feedback (or you might just enjoy learning about cog modeling in R and Stan): fusaroli.github.io/AdvancedCogn...

(only chapter 1-3 are in place, the others will come)

My notes for the advanced cognitive modeling course - 2026

fusaroli.github.io

January 30, 2026 at 9:08 AM

Reposted by Aki Vehtari

Charles Margossian

@charlesm993.bsky.social

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!

January 29, 2026 at 9:55 PM

Reposted by Aki Vehtari

tj mahr 🤘

@tjmahr.com

if you want to know how to compute marginal means from a mixed effects model in brms using rvars, then have I got a supplemental materials document for you pubs.asha.org/doi/10.1044/...

Does the Use of Crowdsourced Listeners Yield Different Speech Intelligibility Results Than In-Person Listeners for Typically Developing Children?

Purpose: We examined the performance of crowdsourced listeners compared with in-person listeners on the measurement of speech intelligibility for...

pubs.asha.org

January 29, 2026 at 10:10 PM

Reposted by Aki Vehtari

Mattan S. Ben-Shachar

@mattansb.msbstats.info

Another absolute banger from @natehaines.bsky.social: generative hierarchical Bayesian modeling* is the best way to investigate individual differences!

*This isn't "just" making use of Bayesian estimation, but one of the best examples I've seen of a full Bayesian embrace of uncertainty.

Nathaniel Haines @natehaines.bsky.social · 9d

1/9 New blog is live! This is part 2 of a series—last time we looked at the Dunning-Kruger effect, now we are digging in to Implicit vs Explicit attitudes and the Implicit Association Test. To start, of course we need a good meme...

haines-lab.com/post/part-2-...

January 29, 2026 at 8:08 AM

Reposted by Aki Vehtari

Nathaniel Haines

@natehaines.bsky.social

1/9 New blog is live! This is part 2 of a series—last time we looked at the Dunning-Kruger effect, now we are digging in to Implicit vs Explicit attitudes and the Implicit Association Test. To start, of course we need a good meme...

haines-lab.com/post/part-2-...

January 26, 2026 at 5:45 PM

Reposted by Aki Vehtari

Solomon Kurz

@solomonkurz.bsky.social

All my #rstats ebooks are now in the quarto format, and hosted on Quarto Pub. Their old bookdown links will all be dead by the weekend.

1/3

January 28, 2026 at 3:34 PM

Reposted by Aki Vehtari

Måns Magnusson

@mansmag.bsky.social

Deadline for StanCon 2026 tutorials and workshops (@mc-stan) is today (anywhere on earth). More information here:
discourse.mc-stan.org/t/stancon-20...

StanCon 2026 Tutorials and Workshop deadline today (AoE)

Hi everyone, The submission deadline for contributing tutorials and workshops is today (anywhere on earth). Tutorial Tutorials provide hands-on learning experiences to help attendees build practica...

Statistical Rethinking Lecture B03 - Adventures in Covariance

January 28, 2026 at 10:12 AM

Aki Vehtari

@avehtari.bsky.social

Our CS department could be a great fit for you, too! Although in the call there is interest in particular topics, all areas of CS are considered including computational Bayes, ML, AI, etc. So worth trying if working in those areas, too

Jukka Suomela @jukkasuomela.fi · 10d

Aalto University is looking for an Assistant Professor in Computer Science, application deadline February 2, 2026!
www.aalto.fi/en/open-posi...

January 26, 2026 at 4:38 PM

Aki Vehtari

@avehtari.bsky.social

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)

**Part 1: From Bayesian inference to Bayesian workflow**

1. Bayesian theory and Bayesian practice
2. Statistical modeling and workflow
3. Computational tools
4. Introduction to workflow: Modeling performance on a multiple choice exam

**Part 2: Statistical workflow**

5. Building statistical models
6. Using simulations to capture uncertainty
7. Prediction, generalization, and causal inference
8. Visualizing and checking fitted models
9. Comparing and improving models
10. Statistical inference and scientific inference

**Part 3: Computational workflow**

11. Fitting statistical models
12. Diagnosing and fixing problems with fitting
13. Approximate algorithms and approximate models
14. Simulation-based calibration checking
15. Statistical modeling as software development

**4. Case studies**

16. Coding a series of models: Simulated data of movie ratings
17. Prior specification for regression models: Reanalysis of a sleep study
18. Predictive model checking and comparison: Clinical trial
19. Building up to a hierarchical model: Coronavirus testing
20. Using a fitted model for decision analysis: Mixture model for time series competition
21. Posterior predictive checking: Stochastic learning in dogs
22. Incremental development and testing: Black cat adoptions
23. Debugging a model: World Cup football
24. Leave-one-out cross validation model checking and comparison: Roaches
25. Model building and expansion: Golf putting
26. Model building with latent variables: Markov models for animal movement
27. Model building: Time-series decomposition for birthdays
28. Models for regression coefficients and variable selection: Student grades
29. Sampling problems with latent variables: No vehicles in the park
30. Challenge of multimodality: Differential equation for planetary motion
31. Simulation-based calibration checking in model development workflow

**Appendices**

A. Statistical and computational workflow for Bayesians and non-Bayesians
B. How to get the most out of Bayesian Data Analysis

January 26, 2026 at 8:18 AM

Aki Vehtari

@avehtari.bsky.social

Publisher's copy-editing for the Bayesian Workflow book has started

January 23, 2026 at 7:45 PM

Reposted by Aki Vehtari

Richard McElreath 🐈‍⬛

@rmcelreath.bsky.social

Adventures in Covariance - Lecture B03 of Statistical Rethinking 2026. Prelude about MCMC effective sample size, Multilevel varying slopes models with correlated features, non-centered parameterizations for covariance models, model expansion workflow. Next week is social networks.

StanCon 2026 Deadline for tutorial and workshop in one week

January 23, 2026 at 1:22 PM

Aki Vehtari

@avehtari.bsky.social

Juho Timonen defended his thesis succesfully 🎉 (the thesis includes two papers I helped with: speeding up ODEs with importance sampling dx.doi.org/10.1002/sta4... and longitudinal GPs doi.org/10.1093/bioi...). Photo includes supervisor Harri Lähdesmäki and the opponent @mrattray.bsky.social

January 23, 2026 at 2:14 PM

Reposted by Aki Vehtari

Måns Magnusson

@mansmag.bsky.social

StanCon 2026 tutorial and workshop deadlines in one week:
discourse.mc-stan.org/t/stancon-20...
@mc-stan.org

Hi everyone, The submission deadline for contributing tutorials and workshops is one week from now, on the 28th of January. Tutorial Tutorials provide hands-on learning experiences to help attendee...

Feb 5th Bayesian Data Analysis Meetup Virtual Event

January 21, 2026 at 9:20 AM

Reposted by Aki Vehtari

MC Stan

@mc-stan.org

February 5th in Bayesian Data Analysis Meetup virtual event:
Bayesian Models in Production by Ian Costley (Wegmans Food Markets)
...hands-on experience deploying Bayesian models built with Stan and PyMC into real production systems...
see more at discourse.mc-stan.org/t/feb-5th-ba...

Hi All, On Feb 5 we have a virtual event for the Bayesian Data Analysis Meetup. We’re excited to host Ian Costley (Wegmans Food Markets) for an upcoming talk on Bayesian Models in Production. Ian wi...

Upcoming Short Course on Bayesian Causal Inference with Stan

January 20, 2026 at 12:15 PM

Reposted by Aki Vehtari

MC Stan

@mc-stan.org

May 11, Short Course on Bayesian Causal Inference with Stan, Stress-Testing Assumptions: Bayesian Methods for Sensitivity Analysis in Causal Inference, in-person at the American Causal Inference Conference meeting in Salt Lake City. See more at discourse.mc-stan.org/t/upcoming-s...

Hi all, I wanted to publicize a short course I’ll be teaching on topics related to Bayesian causal inference. See below for course title, abstract, outline, and other information. The course will be...

Statistical Rethinking 2026 - Lecture B02 - Multilevel Model Expansion

January 20, 2026 at 12:19 PM

Aki Vehtari

@avehtari.bsky.social

I just finished revising the last case study code for the Bayesian Workflow book, which was bigger effort than I expected as I had to do a big revision of the corresponding book chapter, too. Now we are close to being finished

Aki Vehtari @avehtari.bsky.social · 28d

During the last few weeks, Jonah Gabry and I have been reviewing and improving more than 14k lines of code for the Bayesian Workflow book. Still some things to fix, but soon ready

Aki Vehtari @avehtari.bsky.social · Dec 16

We're doing the final sprint, and I think we're able to send the PDF of the forthcoming Bayesian Workflow book to the publisher in the next two weeks (500+ pages), which would mean it would be published some time next year

January 16, 2026 at 6:25 PM

Reposted by Aki Vehtari

Richard McElreath 🐈‍⬛

@rmcelreath.bsky.social

Lecture B02 of Statistical Rethinking 2026. Multilevel model expansion: workflow, cluster and feature engineering, non-centered parameterization, dank memes. Continues next week with models of population covariance and (if there is time) group-level confounding and "Mundlak machines".