index.md

title	author	date	output
Introduction to Bayesian hierarchical modelling using R: course timetable	Andrew Parnell	July 2022 - UCU version	html_document

If you want to learn more about the module please see the introduction. Instructions to install the required software can be found here. All the raw files and code can be found on GitHub (click 'Code' > 'Download ZIP' near the top right if you want offline versions of everything). All links below are in pdf format for you to annotate. A glossary of technical terms is here.

Please note that the course will be recorded so please turn off video if you do not wish to be shown on the recordings.

Day 1: Basic concepts

Time	Class
9:15-10:15	Introduction to Bayesian hierarchical models
10:15-10:30	Break
10:30-11:30	Likelihood and inference
11:30-11:45	Break
11:45-13:00	Guided practical: Loading data in R and running simple analysis (R code)
13:00-14:00	Lunch
14:00-16:00	Self-guided practical (including Breaks) (html) (pdf)

Day 2: Bayesian statistical modelling

Time	Class
9:15-10:15	Introduction to Bayesian statistics
10:15-10:30	Break
10:30-11:30	Bayesian linear and generalised linear models (GLMs)
11:30-11:45	Break
11:45-13:00	Guided practical: Using R, Jags and Stan for fitting GLMs (R code)
13:00-14:00	Lunch
14:00-16:00	Self-guided practical (html) (pdf)

Day 3: Hierarchical modelling

Time	Class
9:15-10:15	Simple hierarchical regression models
10:15-10:30	Break
10:30-11:30	Hierarchical generalised linear models
11:30-11:45	Break
11:45-13:00	Guided practical: Fitting hierarchical models (R code)
13:00-14:00	Lunch
14:00-16:00	Self-guided practical (html) (pdf)

Day 4: Advanced hierarchical models

Time	Class
9:15-10:15	Multi-layer hierarchical models and missing data
10:15-10:30	Break
10:30-11:30	Zero-inflation and multinomial models
11:30-11:45	Break
11:45-13:00	Guided practical: Advanced examples of hierarchical models (R code)
13:00-14:00	Lunch
14:00-16:00	Self-guided practical (html) (pdf)