Bayesian Multilevel Modelling with {brms}

The recording from UseR Oslo's meetup 14/01/2021 www.meetup.com...
[Abstract]
The {brms} package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, a C++ package for performing full Bayesian inference (see mc-stan.org/). The formula syntax is very similar to that of the {lme4} package to provide a familiar and simple interface for performing regression analyses. A wide range of response distributions are supported, allowing users to fit - among others - linear, robust linear, count data, survival, response times, ordinal, zero-inflated, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models, i.e., models with multiple response variables, can be fit as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that reflect their beliefs. Model fits can easily be assessed and compared with posterior predictive checks, cross-validation, and Bayes factors.
[Speaker]
Paul is a statistician currently working as an Independent 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 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 about optimal design and Bayesian data analysis. He has also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University (Finland).
Slides used in the meetup: www.uv.uio.no/...
Timestamps:
To be added.

[@alexandregeorgelustosa5969]

Cool staff! TKS for share!

[@FlavioBarrosProfessor]

Amazing presentation!

[@iirolenkkari9564]

Amazing package. Thank you! 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.

[@UseROslo]

Hi, this channel only focuses on publishing content from the UseR Oslo group. For specific questions about the content, we encourage you to contact the speaker or relevant forums.

[@carlosserrano4048]

On second thought perhaps don’t write me an email