Module manager: TBC
Email: .
Taught: Semester 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable
Year running 2025/26
N/A
| OMAT5201M | Linear Modelling |
| OMAT5203M | Statistical Learning |
| OMAT5205M | Multivariate Methods |
N/A
This module is not approved as an Elective
Bayesian methods in statistics have had a long and often controversial history; nevertheless, they are widely adopted due to their utility in solving complex inference problems. This module introduces the Bayesian approach to statistical inference and decision making.
The objective of this module is to introduce Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. This module also introduces the ideas of quantitative decision theory and rational decision making.
On completion of this module students should be able to:
1. Discuss the differences between Bayesian and traditional statistical methods;
2. Derive prior, posterior and predictive distributions for standard Bayesian models;
3. Employ hierarchical analyses using sampling methods;
4. Produce network representations of joint distributions and perform updates on small networks; and
5. Define utility in the context of decision making and apply decision analysis methods to simple finite dimensional problems.
Skills developed in this module include:
- evaluating the quality of data and selecting suitable methods of analysis.
- fitting models to data and interpreting the results;
- communicating the outcome of data analysis.
Indicative content for this module includes:
1. Degrees of belief and subjective probabilities,
2. The likelihood (choice, exchangeability and the likelihood principle),
3. Prior, posterior and predictive distributions in conjugate analyses,
4. Modelling complex problems with potentially disparate data sources using hierarchical techniques,
5. Bayesian updating using sampling techniques including prior-sample reweighting and Gibbs sampling,
6. Network representations of joint probability distributions and their use in Bayesian updating,
7. Quantitative decision analysis: minimax decisions through to utility theory.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 1 | 1.5 | 1.5 |
| On-line Learning | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | MCQ test and short answer questions | 20 |
| Assignment | Project Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.
There is no reading list for this module
Last updated: 05/02/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team