Introduction to Bayesian Analysis

Introduction to Bayesian Analysis

The course material is accessible via the menu on the left.

The course provides an introduction to Bayesian Statistics and Markov Chain Monte Carlo (MCMC) techniques. MCMC is surprisingly simple, and it allows one to apply more advanced GLMM models.

Commencing with an application of MCMC on simple linear regression models the course slowly proceeds to more advanced generalised linear models (GLM). We show how to implement regression models with temporal correlation, multivariate response variables, breakpoints (to model sudden changes) and multiple variances to deal with heterogeneity. We also discuss models with more exotic distributions like the gamma distribution for continuous data (e.g. biomass) and the beta distribution for proportional data (e.g. coverage).

During the course several case studies are presented, in which the statistical theory is integrated with applied analyses in a clear and understandable manner. Throughout the course MCMC is executed in JAGS (free) via the package R2jags from within R.

Keywords: Introduction to Bayesian Statistics. MCMC. JAGS. R2jags. Multiple linear regression and MCMC. GLM and MCMC. Temporal correlation. Multivariate regression.