ST454 Half Unit
Bayesian Data Analysis
This information is for the 2023/24 session.
Teacher responsible
Dr Sara Geneletti Inchauste Col 5.07
Availability
This course is available on the MPA in Data Science for Public Policy, MSc in Data Science, MSc in Health Data Science, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.
The course will require the use of computers so students must have a laptop.
Pre-requisites
Students must have completed Elementary Statistical Theory (ST102).
Basic knowledge in probability and a first course in statistics such as ST102 or equivalent probability distribution theory and inference. Basic knowledge R or an equivalent programming language required. Students who do not have prior knowledge of R will be required to take an R module with the Digital Skills Lab.
Course content
The course is a hands-on introduction and development of the analysis of Bayesian models with using JAGS, stan and R-INLA with focus on data sets and application. The main topics will be Bayesian regression, Bayesian Hierarchial models and Spatial models. Throughout the course there will be practical examples from epidemiology, public health and social science which will involve data analysis.
Teaching
This course will be delivered using a combination of lectures, seminars and Q&A sessions totalling a minimum of 33 hours in the Winter Term. Week 6 will be used as a reading week.
The course will cover the following
Spatio-temporal data: what is it and why is it useful?
Basics of the R programming language
Bayesian methods, with focus on an intuitive understanding and practical data analysis.
- Bayesian foundations
- Markov Chain Monte Carlo
- Regression (including GLMs)
- Hierarchical Models
- Hamiltonian Monte Carlo
- Integrated Nested Laplace Models
- Spatial models including areal data, ecological regression and spatial prediction
Bayesian software
- JAGS
- stan
- R-INLA
- Discussion of advantages and differences between software
Formative coursework
There will be 5 Moodle quizzes to guide students through some of the more complex analyses.
Indicative reading
Data analysis and regression using multilevel/hierarchical models: Andrew Gelman and Jennifer Hill
Statistical Rethinking: A Bayesian Course with Examples in R and STAN
Spatial and Spatio-temporal Bayesian models with R-INLA: Marta Blangiardo and Michela Cameletti
Assessment
Project (20%, 2500 words) in the WT Week 6.
Presentation (30%, 1500 words) in the WT Week 11.
Project (50%, 6000 words) in the ST Week 2.
Key facts
Department: Statistics
Total students 2022/23: 1
Average class size 2022/23: 1
Controlled access 2022/23: Yes
Value: Half Unit
Course selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Leadership
- Self-management
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Specialist skills