This workshop provides a practical introduction to Bayesian data analysis. We will start by describing the fundamental concepts of Bayesian statistical inference, and contrast these with frequentist methods of inference. Then, using a simple case study, we will explore all the major concepts and issues in Bayesian statistics including priors, likelihood functions, posterior inference, posterior predictive inference, Bayes factors, out-of-sample generalization, and so on. We will then turn to doing Bayesian general and generalized linear regression models and their multilevel counterparts. It is hoped that this workshop will provide a useful introduction to the theory and practice of Bayesian data analysis, and provide an introductory guide on how to use Bayesian methods in psychological research.

Slides

• History of Bayesian Inference
• Introducing Bayesian Inference

Demos

The 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.

Source code

R code to accompany the workshop can be found at https://github.com/lawsofthought/bps-cog-2018-bayes-workshop/tree/master/code.

The demos above are all written in R and Shiny. The source code for these can be found at https://github.com/lawsofthought/psypag-kent-2017/tree/master/shiny.

All code is released according a free and open-source licence, see License.txt for more info.

The LaTeX source code for the slides is also released according to a free and open-source licence (see License.txt) and can be found at https://github.com/lawsofthought/bps-cog-2018-bayes-workshop/tree/master/slides.

GitHub resources

Further resources for this training course can be found on Github at mark-andrews/bps-cog-2018-bayes-workshop.