Please read our student and staff community guidance on COVID-19
Skip to main content Skip to navigation

ST301 Bayesian Statistics and Decision Theory

Lecturer(s): Dr Xavier Didelot

Important: If you decide to take ST301 you cannot then take ST413. Bear this in mind when planning your module selection. Recall: an integrated Masters student must take at least 120 CATS, of level 4+ modules over their 3rd & 4thyears.

Prerequisite(s): Either ST218/219 Mathematical Statistics A&B or ST220 Introduction to Mathematical Statistics

Commitment: 3 lectures per week and one tutorial each in weeks 3, 7 and 9. This module runs in Term 1.

Content: 

This module aims to demonstrate how to build Bayesian models and to train students in the rudiments of decision analysis. Familiarity with this material is very useful for planning a career involving a component of industrial, business or government statistics.

Aims:

  • To understand how Bayesian models are built and evaluated. Appreciate naïve Bayes models and issues such as calibration.
  • To perform basic prior to posterior analysis. To perform discrete prior to posterior inference and beta and Dirichlet conjugate analysis.
  • To understand the foundation of utility theory and apply it in a multi-attribute context. To be able to elicit a utility function.
  • To understand how to model complicated systems in terms of conditional independences. To appreciate the structuring of models through directed acyclic graphs (DAGs). To be able to estimate probabilities in DAGs using conjugate product Dirichlet distributions.

Bibliography:

Assessment: 100% by 2-hour examination.

Examination period: Summer