Paul Bürkner: An introduction to Bayesian multilevel modeling with brms

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The talk is about Bayesian multilevel models and their implementation in R using the package brms. It starts 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 Bayesian models. We then explain how to access Stan using the standard R formula syntax via the brms package. The package supports a wide range of response distributions and modeling options such as splines, autocorrelation, and censoring all in a multilevel context. A lot of post-processing and plotting methods are implemented as well. Some examples from Psychology and Medicine will be discussed.
Paul Bürkner is a statistician currently working as a Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart (Germany). He is the author of the R package brms and a member of the Stan Development Team. Previously, he studied Psychology and Mathematics at the Universities of Münster and Hagen (Germany) and did his PhD in Münster on optimal design and Bayesian data analysis. He has also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University (Finland).
Pual's website: paul-buerkner.github.io/about/
brms: github.com/paul-buerkner/brms

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Опубликовано:

22 апр 2021

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Комментарии : 8

@Sycolog 3 года назад

Thank you so much for building bmrs. You saved my master thesis, got me into Bayesian statistics and made me learn R, which is now a staple tool of my professional career.

@iirolenkkari9564 Год назад

Very valuable package indeed! I'm wondering how to model the covariance structure in a bayesian longitudinal setting, similar to covariance patterns such as compound symmetry, autoregressive, Topelitz etc. in the frequentist world. In the frequentist world, taking serial correlation into consideration narrows the confidence intervals of the parameters. How to model the covariance structure in a bayesian longitudinal setting? I'm wondering if a bayesian intercept always introduces compound symmetry, similar to a random intercept in a frequentist linear mixed effects model? I suspect taking serial correlation would narrow the posterior distributions of the model parameters, strengthening the bayesian inference. However, I'm not at all sure if my thoughts are anywhere near correct. The brms package is a very valuable resource. However, the parts about covariance structures seem to be still in progress. If anyone has good theoretical (and why not practical) bayesian references regarding these covariance modeling issues (serial correlation etc.), I would appreciate them very much.

@doug_sponsler 3 года назад

(1:35) "A lot of us...a lot of us are." The melancholy of that statement was so tangible :)

@XShollaj 2 года назад

That was beautiful - Thank you for the wonderful package, Paul!

@cyruskavwele5304 2 года назад

Is it possible to include a factor variable in the model? If yes any examples please.

@musiknation7218 9 месяцев назад

How to consider priors in Bayesian regression with some data

@Eizengoldt 4 месяца назад

Dont know

@jujuchristov1693 Месяц назад

Anyone know why loo_predict(blm) and predict(blm[-obs.,],data[obs,]) are giving me a predicted odds of 0.28 and 0.8 respectively? These estimates are so far apart. “0.8” seems more accurate to me but the events true outcome was “0” so loo_predict did a better job. Does loo_predict just not work with high Pareto values, is that why??