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Bayesian Statistical Methods (POPH90139)

Graduate courseworkPoints: 12.5Not available in 2019

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Overview

Year of offerNot available in 2019
Subject levelGraduate coursework
Subject codePOPH90139
FeesSubject EFTSL, Level, Discipline & Census Date

Topics include: simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of non-informative prior distributions; the relationship between Bayesian methods and standard ‘classical’ approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; application of Bayesian methods for fitting hierarchical models to complex data structures.

Intended learning outcomes

To achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems.

Generic skills

Independent problem solving, facility with abstract reasoning, clarity of written expression, sound communication of technical concepts

Last updated: 26 June 2019