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Programme & Unit Catalogues

MA50247: Bayesian and large scale methods

[Page last updated: 03 August 2022]

Academic Year: 2022/23
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Semester 1
Assessment Summary: CW 100%
Assessment Detail:
  • Coursework 1 (CW 50%)
  • Coursework 2 (CW 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Learning Outcomes: Students should be able to: formulate structured large scale Bayesian models; generate random samples efficiently from such models; analyse the structure of a Bayesian model in order to design a computational method for numerical inference; interpret and analyse the output of a Bayesian simulation or direct calculation algorithm.
To communicate: problem descriptions; model formulation; and inferences.

Aims: To introduce methods for large scale Bayesian stochastic modelling and statistical inference, including theoretical concepts as well as computational techniques.
To develop independent problem solving skills.

Skills: Problem solving (T,F&A), computing (T,F&A), written and oral presentation (F&A).

Content: Bayesian modelling, inference and prediction.
Bayesian model assessment.
Large models and computational methods utilising sparsity and Markov properties.
Directed (hierarchical) and undirected graph models.
Designing efficient Markov chain Monte Carlo (MCMC) samplers.
MCMC output diagnostics.
Numerical techniques for direct, non-sampling Bayesian methods.

Programme availability:

MA50247 is Optional on the following programmes:

Department of Mathematical Sciences


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