Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses, many researchers do not have opportunities to learn the fundamentals of Bayesian methods, which makes using Bayesian data analysis in practice more challenging. This seminar provides an introduction to Bayesian methods, both theoretically and practically. We will overview the fundamental concepts of Bayesian inference and Bayesian modelling, including how Bayesian methods differ from their classical statistics counterparts, and show how to do Bayesian data analysis in practice in R.

Slides

The slides for this seminar are here. These slides contain links to the following online demos:

1. Binomial test for coin toss data.
2. Binomial likelihood for coin toss data.
3. Bayesian inference for coin toss data.

Binder RStudio Server

An RStudio server session with Stan and brms installed and ready to use is available by clicking this button. In there, you will find all the scripts in this repository, including an example brms script.

Installing Stan and brms

Instructions on how to install Stan and brms on your machine is here.

GitHub resources

Further resources for this training course can be found on Github at mark-andrews/intro_bda_qub.