Suspended in 2025/26
ST454      Half Unit
Bayesian Data Analysis

This information is for the 2025/26 session.

Course Convenor

Dr Sara Geneletti Inchauste

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.

Requisites

Pre-requisites:

Students must have completed ST102 before taking this course.

Additional requisites:

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  Nimble (similar to BUGS), 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.

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 

  • Nimble (similar to BUGS)
  • Stan
  • R-INLA
  • Discussion of advantages and differences between software

Teaching

20 hours of lectures and 10 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Formative assessment

There will regular workshops where students have to replicate the analyses covered in lectures in different data sets

 

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

Presentation (30%, 1500 words) in Winter Term Week 11

Project (20%, 2500 words) in Winter Term Week 6

Project (50%, 6000 words) in Spring Term Week 2


Key facts

Department: Statistics

Course Study Period: Winter Term

Unit value: Half unit

FHEQ Level: Level 7

CEFR Level: Null

Total students 2024/25: 1

Average class size 2024/25: 1

Controlled access 2024/25: No
Guidelines for interpreting course guide information

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