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MA50247: Bayesian and large scale methods

[Page last updated: 15 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Mathematical Sciences
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Coursework 1 (CW 50%)
  • Coursework 2 (CW 50%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites 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.
Further information on programme availabilityProgramme availability:

MA50247 is Optional on the following programmes:

Department of Mathematical Sciences

Notes:

  • This unit catalogue is applicable for the 2020/21 academic year only. Students continuing their studies into 2021/22 and beyond should not assume that this unit will be available in future years in the format displayed here for 2020/21.
  • Programmes and units are subject to change in accordance with normal University procedures.
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