Bayesian statistics with R

Who: Olivier Gimenez
When: March 22-23-25-26, 2021
Nov 25-Dec 2-Dec 9, 2021
April 11-12, 2022
Sept 22-23, 2022
Nov 24-Dec 1-Dec 8, 2022~~

Where: Zoom
Biodiversity Ecology Evolution Master, Montpellier University
Collège Doctoral, Montpellier University
Collège Doctoral, Montpellier University
Biodiversity Ecology Evolution Master, Montpellier University


  1. An introduction to Bayesian inference [lecture | practical 1 | video]
  2. The likelihood [lecture | practical 2 | video]
  3. Bayesian analyses by hand [lecture | video]
  4. A detour to explore priors [lecture | practical 3 | practical 4| video]
  5. Markov chains Monte Carlo methods (MCMC) [lecture | video]
  6. Bayesian analyses in R with the Jags software [lecture | R script | practical 5 | practical 6 | video]
  7. Contrast scientific hypotheses with model selection [lecture | practical 7 | video]
  8. Heterogeneity and multilevel models (aka mixed models) [lecture | R script | practical 8 | practical 9 | video]

Learning objectives


install.packages(c("tidyverse", "R2jags", "here", "lme4", "visreg", "lattice", "brms"))


Many slides are from a workshop we used to run a loooong time ago with Ruth King, Byron Morgan and Steve Brooks. I also re-used or adapted slides by Richard McElreath, Kerrie Mengersen, Francisco Rodriguez Sanchez, Jim Albert and Jingchen Hu, Tristan Marh, Jason Matthiopoulos, a paper by Michael McCarthy and Pip Masters, Andrés Lopez-Sepulcre and John Kruschke’ book cover. The sources for the images are James Kulich, Matt Buck, xkcd and Mike West.


If you spot a typo or an error, find a bug, want to suggest changes, or have trouble running the code, please file an issue or get back to me


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Any computer code (R, HTML, CSS, etc.) in slides and worksheets, including in slide and worksheet sources, is also licensed under MIT.