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

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