MT5731 Advanced Bayesian Inference

Academic year

2024 to 2025 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 11

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

Lectures: co-taught with MT4531. 10.00 am Mon (odd weeks), Wed and Fri

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

Module coordinator

Dr N Margaritella

Dr N Margaritella
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 examines the Bayesian framework for analysing statistical problems, including an introduction to the latest theoretical and practical developments in the field. The syllabus includes Bayes' theorem, standard inference for conjugate Bayesian analyses, prediction, model comparison, principles of Bayesian computational techniques and software, and Markov chain Monte Carlo theory and applications. Instruction of advanced aspects of the Bayesian framework theory and its application is carried out by guided independent study, involving completion of a substantial project.

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 MT4531 OR TAKE MT5831

Assessment pattern

2-hour written examination = 60%, Coursework = 40%.

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 hours of lectures (10 weeks), 1-hour tutorial (9 weeks);

Scheduled learning hours

34

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

Guided independent study hours

116

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 and use appropriate Bayesian statistical software, for example NIMBLE
  • Select a hypothesis and perform model comparison
  • Gain in-depth knowledge of an advanced topic of Bayesian inference