58吃瓜

MT4531 Bayesian Inference

Academic year

2026 to 2027 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

10.00 am Mon (weeks 1, 3, 5, 7, 9, 12), Wed and Fri

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module Staff

TBD

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module is intended to offer a re-examination of standard statistical problems from a Bayesian viewpoint and an introduction to recently developed computational Bayes methods. The syllabus includes Bayes' theorem, conjugate analyses for different likelihoods and prior distributions, univariate Normal linear regression, multiple regression, principles of Bayesian computational, Markov chain Monte Carlo 鈥� focussing on the underlying theory 鈥� and Bayesian non-parametrics.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3507 OR PASS MT3508

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT5731 OR TAKE MT5831

Assessment pattern

Examination = 90%, Coursework = 10%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

24 lectures and 7 practical classes over the semester.

Scheduled learning hours

31

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

119

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Explain the principles that underline the Bayesian statistical paradigm
  • Use the rules of probability to update beliefs for statistical model parameters given a set of observations, explain the main principles that underline the elicitation of expert beliefs, and use the rules of Bayesian statistics to predict future events
  • Explain the main computational algorithms for implementing Bayesian statistical inference
  • Derive the posterior distribution of linear model parameters, and perform model comparison between linear or any other models
  • Explain the main principles and ideas that underpin Bayesian non-parametric modelling