Bayesian statistics using bmrs in R

News:Online training: Bayesian statistics using bmrs in R


Online training – Computational Bayesian methods using brms in R

Where and When: Online (Zoom) – 14-18 February 2022

This course provides a relatively accessible and technically non-demanding introduction to the basic workflow for fitting different kinds of linear models using a powerful front-end R package for Stan called brms.

We assume familiarity with R. Participants will benefit most if they have previously fit linear models and linear mixed models (using lme4) in R, in any scientific domain. No knowledge of calculus or linear algebra is assumed, but basic school level mathematics knowledge is assumed (this will be quickly revisited in class).

  • become familiar with the foundations of Bayesian inference

  • be able to fit a range of multiple regression models and hierarchical models for normally distributed data, for log-normal, and binomially distributed data.

  • be able to communicate the results of a Bayesian analysis
  • know how to select priors for their models using prior predictive checks
  • know how to assess the descriptive accuracy of a model using posterior predictive checks.

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