Module Overview

Bayesian Learning

This module will introduce Bayesian analysis with emphasis on data modelling and computational methods. After an overview of foundational concepts in probability theory, students will be introduced to the basic concepts in Bayesian analysis including prior specification, posterior inference prediction and model selection. Monte Carlo methods will be used to approximate quantities of interest. All the important concepts and methods will be explained via examples using advanced statistical software (e.g., R). 

Module Code

MATH 3814

ECTS Credits


*Curricular information is subject to change

Probability theory

Review of Bayes’ theorem and basic probability theory. Monte Carlo methods. Likelihood principle. Subjective probability.

Probabilistic modelling

Conjugate models (e.g. Beta-Binomial, Gamma-Poisson). Prior specification. Posterior inference and prediction. Bayes factor. Linear regression.

Simulation methods

Markov chain Monte Carlo algorithms (e.g. Metropolis-Hastings, Gibbs sampler).

Software packages

Data analysis using advanced statistical packages (e.g. R, RStan).

Lectures supported by data analysis sessions and the use of statistical software packages. 

Module Content & Assessment
Assessment Breakdown %
Other Assessment(s)100