MAS8405 : Bayesian Data Analysis

Semester 2 Credit Value: 10
ECTS Credits: 5.0


Bayesian inference provides an ideal approach for synthesizing information from different sources in a coherent way. In recent years great advances have been made in the application of Bayesian statistical inference to problems across a wide variety of areas within industry, such as drug development, voice recognition and credit card fraud detection. This has been made possible by the development of computational algorithms which allow posterior distributions to be found in complicated models. This module starts with an introduction to the basics of Bayesian inference before moving on to more complex models relevant to practical data analysis.

Specifically, the module aims to equip students with the following knowledge and skills:
-       To gain an understanding of the principles and the practical applications of the Bayesian approach to data analysis.
-       To gain knowledge of modern Bayesian computational methods and their application to industrial problems.

Outline Of Syllabus

-       Conjugate Bayesian inference
-       Non-conjugate models
-       Markov chain Monte Carlo (MCMC); assessment of mixing and convergence
-       Posterior summaries
-       Applications, such as: linear models, generalized linear models, mixture models, hidden Markov models, dynamic linear models, Gaussian process regression
-       Computation using R packages such as rjags

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials91:3013:30Non-synchronous online pre-recorded lectures and set reading
Guided Independent StudyAssessment preparation and completion214:0028:00Formative and summative reports
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in person structured synchronous practical
Guided Independent StudyProject work129:0029:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery22:004:00present in person drop-in
Guided Independent StudyIndependent study91:3013:30Lecture follow-up/background reading
Teaching Rationale And Relationship

Pre-recorded lectures and set reading are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. There are two present-in-person practical sessions per week to ensure rapid feedback on understanding. Scheduled present-in-person drop-ins provides opportunity to ask questions and receive immediate feedback. Students unable to attend PiP will be able to complete the practical work at home and will be able to receive immediate feedback through joining the drop-ins virtually.

Alternatives as described will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.

Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report2M40Individual report
Report2M60Main module project
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA 3 min video articulating the main findings of one aspect of the coursework report
Formative Assessments
Description Semester When Set Comment
Practical/lab report2MA compulsory report allowing students to develop problem solving techniques and to practise the methods learnt and to assess progres
Assessment Rationale And Relationship

A compulsory formative practical report allows the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessments.

The oral presentation encourages students to focus on interpretation of statistical results, builds their skills in the presentation of statistical concepts, and provides opportunity for feedback.

In a foundational subject like the Mathematical Sciences, there is research evidence to suggest that continual consolidation of learning is essential and the fewer pieces of assessment there are, the more difficult it is to facilitate this. On this module, it is particularly important that the material on the earlier summative assessment is fully consolidated, before the later assessment is attempted.

Reading Lists