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Module

MAS3929 : Bayesian Statistics and Decision Theory

  • Offered for Year: 2025/26
  • Module Leader(s): Professor Kevin Wilson
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 1 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System

Aims

To gain an understanding of the principles of Bayesian statistics and practical applications of more complex models relevant to practical data analysis. To be introduced to the principles of Bayesian decision theory.

Outline Of Syllabus

Review of Bayesian inference for singular parameter models. Inference for multi-parameter models using conjugate prior distributions: mean and variance of a normal random sample. Introduction to Markov chain Monte Carlo methods: Gibbs sampling, Metropolis-Hastings sampling, mixing and convergence. Application to regression modelling such as linear models, generalised linear models and extensions. Computation using R. Principles of Bayesian decision theory.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion151:0015:00Completion of in-course assessments
Scheduled Learning And Teaching ActivitiesLecture21:002:00Revision Lectures
Scheduled Learning And Teaching ActivitiesLecture201:0020:00Formal Lectures
Scheduled Learning And Teaching ActivitiesPractical51:005:00Practical Classes
Guided Independent StudyIndependent study581:0058:00Preparation time for lectures, background reading, coursework review
Total100:00
Teaching Rationale And Relationship

The teaching methods are appropriate to allow students to develop a wide range of skills, from understanding basic concepts and facts to higher-order thinking. Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practical classes are used to help develop the students abilities at applying the theory to solving problems.

Assessment Methods

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

Exams
Description Length Semester When Set Percentage Comment
Written Examination1201A80N/A
Other Assessment
Description Semester When Set Percentage Comment
Report1M20PROJECT: Application of Bayesian statistics, written up in a report.
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Report1MMINI-PROJECT: Application of Bayesian statistics, written up in a short report - preparation for the summative project.
Assessment Rationale And Relationship

A substantial formal unseen examination is appropriate for the assessment of the material in this module.

The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes. The assurance of academic integrity forms a necessary part of the programme accreditation.

Examination problems may require a synthesis of concepts and strategies from different sections. The examination time allows the students to try different strategies, work out examples and gather evidence for deciding on an effective strategy, while carefully articulating their ideas and explicitly citing the theory they are using.

The project allows the students to develop their problem solving techniques, to practice the methods learnt in the module, to assess their progress and to receive feedback; this assessment has a secondary formative purpose as well as its primary summative purpose.

Reading Lists

Timetable