Dr Sara Geneletti Inchauste Col 5.07

This course is available on the MSc in Data Science, MSc in Health Data Science, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), 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.

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.

The course is a hands-on introduction and development of the analysis of Bayesian spatial and spatio-temporal models with focus on data sets and application. The main topics will be spatio-temporal data, Bayesian models for spatio-temporal data, Integrated nested Laplace approximations, analysing spatio-temporal models using R-INLA a special package specifically designed for Bayesian spatio-temporal models. Throughout the course there will be practical examples from epidemiology, public health and social science which will involve data analysis.

This course will be delivered using a combination of lectures, seminars and Q&A sessions totalling a minimum of 33 hours in the Lent Term. This year some of the course will be delivered through a combination of workshops (possibly virtual), flipped-lectures in the form of short online videos and synchronous Q&A sessions.

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

• Bayes Theorem
• Prior and posterior distributions
• MCMC methods
• Integrated Nested Laplace Models
• Regression (including GLMs)
• Hierarchical Models

Spatio-temporal modelling

• Spatial models including areal data, ecological regression and spatial prediction
• Spatio-temporal models including disease mapping 

Bayesian software 

• R - INLA
• JAGS

There will be 5 Moodle quizzes to guide students through some of the more complex analyses.

Spatial and Spatio-temporal Bayesian models with R-INLA: Marta Blangiardo and Michela Cameletti

Data analysis and regression using multilevel/hierarchical models: Andrew Gelman and Jennifer Hill

Project (20%, 2500 words) in the LT Week 6.
Presentation (30%, 1500 words) in the LT Week 11.
Project (50%, 6000 words) in the ST Week 2.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.