ST308      Half Unit
Bayesian Inference

This information is for the 2020/21 session.

Teacher responsible

Dr Konstantinos Kalogeropoulos COL.610


This course is available on the BSc in Actuarial Science, BSc in Business Mathematics and Statistics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. 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 Inference: 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, Variational Bayes, Monte Carlo methods), Markov Chain Monte Carlo (MCMC) simulation (Gibbs sampler, Metropolis-Hastings algorithm). Computer tools (R).

Applications: Linear models in Regression and Classification (Bayesian Linear Regression, Generalized Linear Models, Logistic Regression), Hierarchical/ Multilevel Models, Cluster Analysis and Mixture Modeling.


This course will be delivered through a combination of classes and lectures totalling a minimum of 29 hours across the Lent Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course does not include a reading week and will be concluded by the end of week 10 of Lent Term.

Formative coursework

Optional problem sets and computer exercises.

Indicative reading

J.K. Kruschke, Doing Bayesian Data Analysis. An tutorial with R, JAGS and Stan. 2nd edition.

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 summer exam period.
Project (20%) in the ST.

Student performance results

(2017/18 - 2019/20 combined)

Classification % of students
First 39.3
2:1 34.4
2:2 13.5
Third 8.6
Fail 4.3

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2019/20: 67

Average class size 2019/20: 22

Capped 2019/20: Yes (70)

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

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