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Webinar: An introduction to Bayesian multilevel modeling with brms

This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms. We will start with a short introduction to multilevel modeling and to Bayesian statistics in general followed by an introduction to Stan, which is a flexible language for fitting open-ended...

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Discuss our new R-hat paper for the journal Bayesian Analysis!

Here’s your opportunity: We welcome public contributions to the Discussion of the manuscript the manuscript Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC by A. Vehtari, A. Gelman, D. Simpson, B. Carpenter and P. C. Bürkner, which will be featured as a Discussion Paper in the June 2021 issue of the journal. You can find the manuscript in the Advance publication section of the...

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The Folk Theorem, revisited

It’s time to review the folk theorem, an old saw on this blog, on the Stan forums, and in all of Andrew’s and my applied modeling. Folk Theorem Andrew uses “folk” in the sense of being folksy as opposed to rigorous. The Folk Theorem of Statistical Computing (Gelman 2008): When you have computational problems, often there’s a problem with your model. Isn’t computation often the culprit? Better samplers like the no-U-turn...

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Simulation-based calibration: Two theorems

Throat-clearing OK, not theorems. Conjectures. Actually not even conjectures, because for a conjecture you have to, y’know, conjecture something. Something precise. And I got nothing precise for you. Or, to be more precise, what is precise in this post is not new, and what is new is not precise. Background OK, first for the precise part (which is not new): Simulation-based calibration. You have a computer program to get posterior...