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MA50247: Bayesian and large scale methods

Academic Year: 2018/9
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)
Period:
Semester 2
Assessment Summary: CW 40%, EX 50%, OR 10%
Assessment Detail:
  • Coursework (CW 40%)
  • Examination (EX 50%)
  • Oral coursework presentation (OR 10%)
Supplementary Assessment:
MA50247 Coursework / Exam (dependent on failed component)component) (where allowed by programme regulations)
Requisites: Before taking this module you must take MA40198
Description: 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.

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.

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

Notes:

  • This unit catalogue is applicable for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this unit will be available in future years in the format displayed here for 2018/19.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Undergraduates: .
  • Postgraduates: .