Bayesian Data Analysis
Date: August 28, 2018
Location: Liverpool, UK
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
Demos
The slides contain links to the following online demos:
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.