Introduction to Bayesian Data Analysis with R
- Level(s) of Study: Short course
- Start Date(s): To be confirmed
- Duration: 9:30 am – 5:30 pm
- Study Mode(s): Short course
- Campus: City Campus
On this three-day course, you will gain a solid introduction to Bayesian methods, both theoretically and practically. We will teach 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.
This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science, including the social sciences, life sciences, physical sciences. No prior experience or familiarity with Bayesian statistics is required.
Level: CPD, Advanced / Professional
The course will cover these key topics:
- an overview of what Bayesian data analysis is, how it fits into practical data analysis and statistics, and how Bayesian approaches can be blended with traditional classical approaches to statistics
- introduction to Bayes’ rule and how they can be used as a means for performing statistical inference
- Bayesian analysis of normal linear regression models, which can be used to illustrate important and interesting parallels between Bayesian and classical or frequentist analyses, giving two different perspectives on the same problem
- application of Markov Chain Monte Carlo (MCMC) to Bayesian inference in practice using the acclaimed `brms` package in R
- Bayesian model comparison using cross-validation, information criteria, Bayes factors
- application of Bayesian methods to generalised linear models
- application of Bayesian methods to multilevel and mixed effects models
This course will take 6 contact hours per day plus two 2-hour breaks.
The sessions will be as follows:
- Session 1: 9:30am-11:30am;
- Session 2: 12:30am-2:30pm;
- Session 3: 3:30pm-17:30pm.
Tutor Profile: Mark Andrews is an Associate Professor at Nottingham Trent University whose research and teaching is focused on statistical methodology in research in the social and biological sciences. He is the author of 2021 textbook on data science using R that is aimed at scientific researchers, and has a forthcoming new textbook on statistics and data science that is aimed at undergraduates in science courses. His background is in computational cognitive science and mathematical psychology.
Other available online CPD courses in this series include:
"Mark was good at explaining the concepts clearly and the code and worked examples consolidated the knowledge. He was also responsive to questions. I enjoyed the course and feel confident in using the brms package for my own research."