Introduction to the Bayesian framewrk for biometrics

Welcome to the 2021 PhD training course from the Digital Public Health programm at the University of Bordeaux.

Slide decks

1. slide deck #1: Bayesian theory
2. slide deck #2: Bayesian computations

Practicals

• Exercise 1: Monte-Carlo -- solutions
• Exercise 2: Inverse transform -- solutions
• Exercise 3: Metropolis-Hastings algorithm -- solutions
• Exercise 4: BUGS & JAGS -- solutions
• Exercise 5: Post-mortem analysis of an under-powered randomized trial and Goligher article -- solutions

Lecture notes

here

Technical requirements

1. have an up-to-date working installation of R:
   • latest version of R (≥ 4.0) 👉 https://cran.r-project.org/
   • latest version of RStudio (≥ 1.4) 👉 https://www.rstudio.com/products/rstudio/download/#download
2. have JAGS software installed and linked to R:
   • install the JAGS software from here 👉 https://sourceforge.net/projects/mcmc-jags/files/
   • install the rjags package in R
   • make sure it works: the command library(rjags) should give the following output:
     ## Loading required package: coda
## Linked to JAGS 4.3.0
## Loaded modules: basemod,bugs
3. have the following R packages installed: coda, jagsUI, MCMCvis

Pre-requisites

• Maximum Likelihood estimation
• R functional programming