MT4571 Applied Bayesian Statistics

Academic year

2025 to 2026 Semester 1

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

To be confirmed

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 Staff

Dr Nicolò Margaritella; Dr Michail Papathomas

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

Module description

Bayesian statistics has become the predominant statistical theory in the digital era and an essential data analysis tool for modern statisticians and computer scientists in both academia and industry. This module introduces the students to advanced statistical methods within the Bayesian framework. The applied nature of the course provides the students with experience in conducting a variety of Bayesian analyses spanning from linear models to hierarchical models, mixture models and nonparametric models. Computer laboratory sessions will allow students to consolidate fundamental skills to conduct a variety of Bayesian analyses on different datasets.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3507 OR PASS MT3508

Assessment pattern

Examination = 50%, Coursework = 50%

Re-assessment

Oral Examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks) and 1 practical (x 10 weeks).

Scheduled learning hours

35

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

Guided independent study hours

117

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

Intended learning outcomes

  • Demonstrate a practical understanding of Bayesian methods.
  • Understand the principles that underpin standard and advanced MCMC samplers available in NIMBLE.
  • Understand and fit Bayesian Linear Models and Generalised Linear Models with NIMBLE.
  • Select and elicit prior distributions, select models through Bayesian variable selection and the WAIC criterion, and run posterior model checks.
  • Understand and fit advanced models such as hierarchical models, mixture models and Bayesian nonparametric models with NIMBLE.
  • Interpret and report results for both a technical audience and the general public.