Richard McElreath 🐈‍⬛

Recording now available: www.youtube.com/watch?v=u8vQ...

**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

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)

screen shot of lecture playlist links and table of lecture topics

Possums in your yard? News got you down? Fear not, I have made playlists for the A and B sections of my ongoing Statistical Rethinking course. Click the section of your choice, sit back, and forget the possums and decay of the international order while your brain updates. github.com/rmcelreath/s...

YouTube video by Richard McElreath Statistical Rethinking Lecture B03 - Adventures in Covariance

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.

It's cold here. Time for gulasch I think.

short skirt, good for striding

YouTube video by Clips Lord of the Rings (1978) - Ralph Bakshi - Strider

best strider is bakshi strider! www.youtube.com/watch?v=1t91...

Everywhere placental mammals drive out marsupials. But the pouchy possum, Opossum!, thrives amidst the placentas. It is a survivor, honed by evolution to live on a diet of ticks and vitriol. It's life is a cathedral to perseverance. Truly the cockroach of marsupials. Pay respect, but do not approach

Three major research universities opt out of new Elsevier deal Complaints over ‘price increases’ and open access models spur UK institutions to walk away from offer from publishing giant, despite nationally negotiated agreement

And so it begins... 3 UK universities (Essex, Sussex & Kent) have just gone public about walking away from their Elsevier Read & Publish deals, despite Jisc's recently announced agreement. Expect to see more of these over the coming months. www.timeshighereducation.com/news/three-m... #OpenAccess

excited man reaction meme top: ordinary fixed effects priors for two vectors of parameters middle: partial pooling priors, independent for both vectors bottom: multivariate partial pooling prior relating vectors to one another through a covariance matrix

I take statistics very seriously

wieso sagt man na mensch das ist ja ne geile quadratische funktion und nicht irreparabel !

For the 1 person who might be interested, see line 279 at this link for the full scoop euripidesscholia.org/Edition/Ores...

I took 2 years of ancient Greek in college, so I know this "weasel" story. The pedant in me must correct the wiki summary to point out that the Greek does not contain the word "sea", just "calm (conditions)": ἐκ κυμάτων γὰρ αὖθις αὖ γαλήν’ ὁρῶ. "From the waves I see calmness again" or similar

YouTube video by Richard McElreath Statistical Rethinking 2026 - Lecture A03 - Geocentric Models

Gauss says you may have a little orbital dynamics as a treat: Lecture A03 of Statistical Rethinking 2026. History of least squares estimation, simple linear regression, quadratic approximation, prior predictive distributions. www.youtube.com/watch?v=JX_U...

My cat Mischka has never once read a news, sits atop the kitchen cupboard and does happy air biscuits

University of California, Berkeley (UCB) is hiring:
Assistant/Associate/Full Adjunct Professor - Epidemiology and Biostatistics

Beautiful business research people: The Leibniz Information Centre for Economics has created an accessible introduction to Open Science that you can download in both English and German here: expedition-open-science.org

You can even order the German print version for free!

YouTube video by Richard McElreath Statistical Rethinking 2026 - Lecture B02 - Multilevel Model Expansion

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".

Einsamkeit zum Schnäppchenpreis!

I don't know why I am still surprised by this stuff. Anyway, it's a very interesting book so far.

predominantly ‘Jewish’ ­ human psy­ chol­ ogy with a new psy­ chol­ ogy that was ‘properly German.’ ”51 Lorenz joined the Nazi party in June of 1938. By 1940, he was appointed to the Kant Professorship of Psy­ chol­ ogy at the Albertus University of Königsberg in East Prus­ sia, which came along with the directorship of an Institute of Comparative Psy­ chol­ ogy. Adapting his domestication theme to the Nazi context, Lorenz wrote two articles in 1940 making explicit connections between Nazi racial hygiene and his own studies of animal be­ hav­ ior; the second of ­ these was published in the official Nazi journal for biology teachers.52 Lorenz quickly became part of the Nazi war machine. In Oc- tober 1941, just eight months ­ after assuming his Königsberg position, he was drafted into the German Army, first as a military psychologist in Poznan, a city in western Poland taken over by the Nazis ­ after their inva- sion of Poland in 1939. In Poznan, as a member of the Nazi Office of Race Policy, Lorenz administered ­ mental tests aimed at sorting “mixed Poles and Germans into German-­ like ­ people that could be rehabilitated and Poles who could not,” and who ­ were subsequently sent to concentration camps.53 When such psychological testing was discontinued, he worked as a physician and psychiatrist at the reserve hospital in Poznan.54 Along with ­ these practical activities, Lorenz continued to support Nazi ideology through intellectual ­ labors. In 1943, he published another article extol- ling the virtues of race purity, and warning of the disruptive effects of do- mestication on animals’ instincts.

I am reading "Killer Instinct: The popular science of human nature in 20th century America" by Nadine Weidman, and I am learning a lot. I was aware that Konrad Lorenz (father of field of ethology, winner of 1973 Nobel prize) was a Nazi, but didn't know he was so enthusiastic about it.

The ENDOW project is seeking a Research Officer in Research Data Management to be based at the London School of Economics. - Oversee and extend our database covering geospatial, demographic, economic & social network data from 50+ communities in 30+ countries! - Contribute to research on the dynamics of social and economic inequality! - Join a stellar interdisciplinary team! - funded by the ESRC and the NSF [world map showing location of ENDOW communities; photos of some of the team members and some of the ENDOW communities showing their diversity

Research Officer in Research Data Management Salary from £43,277 to £48,220 pa inclusive of London allowance Fixed-term appointment for 18 months This research officer position is to work with Eleanor Power as part of the ongoing "ENDOW" project (Economic Networks and the Dynamics of Wealth Inequality), funded by the UKRI and US National Science Foundation. The research officer will be based in the Department of Methodology, a leading centre for research and education in social science research methods. While we expect this to be a full-time appointment, we are open to considering the possibility of a part-time appointment. The post entails overseeing and extending the database of the "ENDOW" project, which includes data from over fifty communities around the world, comprising economic, demographic, and social network data characterising each community, its resident households, and the individuals within them. The research officer will be responsible for the expansion of this database to include longitudinal data from each community site, implementing a robust data infrastructure to manage and curate this information. The research officer will work closely with Dr Power and data contributors to ensure data quality, standardisation, and appropriate governance. They will contribute to core analyses of the ENDOW project and develop tools and resources that will be of wide utility for the collection, curation, and analysis of cross-cultural data. This role is central to building the data infrastructure that will enable meaningful, productive comparisons across these diverse field sites and will ultimately serve as a resource for the broader scientific community. The successful candidate will: Have a completed PhD, be close to completing a PhD, or have other research experience that demonstrates the capability to produce independent original research Have experience with database design, data management and data governance Have experience with programming in R or Python

🚨Job alert! The ENDOW project is hiring a Research Officer in Research Database Management, to be based at @lsemethodology.bsky.social.

Oversee & expand our database & contribute to research on social & economic inequality.

Deadline 15 February. Share & reach out!
jobs.lse.ac.uk/Vacancies/W/...

This is the first thing I thought of and I wont be explaining how

Background material for you

The river thaws and the ducks hold court

pedal*

Yeah her piece "Hawaii Oslo" does clever stuff with overtones as well. Half the song has a D peddle tone with the rest playing off the overtone series it builds.

PCA is one of those techniques that sounds unreasonable (yeah sure we all want to fit a p-dimensional ellipsoid to our data) but turns out to be a reasonable solution to a wide range of problems.

Also: I do hope stackexchange has been archived, because it is my gen's library of alexandria

YouTube video by Are We Europe Hania Rani - Esja | Are We Europe Live Sessions

Let me interrupt your sadscroll. I really like this song. It's nothing fancy, a familiar E minor progression (Em Bm A Em C) for the most part. But the voicing and layering is really satisfying somehow. www.youtube.com/watch?v=ys-7...

YouTube video by Richard McElreath Statistical Rethinking 2026 - Lecture A02 - Garden of Forking Data

I biked to work in 3cm of slush this morning to deliver another lecture. Stat Rethinking A02: more Bayesian updating, points and intervals, posterior predictive distributions. Next week it's linear regression as Gauss intended. Full course schedule here: github.com/rmcelreath/s...