ST308      Half Unit
Bayesian Inference

This information is for the 2017/18 session.

Teacher responsible

Dr Wai-Fung Lam COL 6.09


This course is available on the BSc in Accounting and Finance, BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Mathematics with Economics and BSc in Statistics with Finance. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.


Students must have completed Mathematical Methods (MA100) and Elementary Statistical Theory (ST102).

ST202 is also recommended.

Course content

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, Frequentist risk.

Bayesian Analysis: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and hypothesis testing, Bayes factors and model selection. Comparison with Frequentist approaches.

Implementation: Asymptotic approximations (Laplace approximation, Monte Carlo methods, stochastic simulation), Markov Chain Monte Carlo (MCMC) simulation (Gibbs sampler, Metropolis-Hastings algorithm). Computer tools (R, WinBUGS). Illustration via applications in Regression (Linear, ANOVA, Multiple, Generalized Linear Models), Hierarchical/ Multilevel Models and Time Series.


20 hours of lectures and 9 hours of computer workshops in the LT. 2 hours of lectures in the ST.

There will be no reading week in week 6, but there will be no lectures and classes in week 11.

Formative coursework

Optional problem sets and computer exercises.

Indicative reading

P.M. Lee, Bayesian Statistics. An introduction.

J.O. Berger, Statistical Decision Theory and Bayesian Analysis.

D. Gamerman, H. F. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference

A. Gelman, Bayesian data analysis.


Exam (80%, duration: 2 hours) in the main exam period.
Project (20%) in the ST.

Student performance results

(2014/15 - 2016/17 combined)

Classification % of students
First 35.1
2:1 23.6
2:2 20.9
Third 13.5
Fail 6.8

Key facts

Department: Statistics

Total students 2016/17: 51

Average class size 2016/17: 26

Capped 2016/17: Yes (60)

Lecture capture used 2016/17: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

PDAM skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Application of numeracy skills
  • Specialist skills