Postgraduate - UnitEPM5013 - Bayesian statistical methods

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Level	Postgraduate
Faculty	Faculty of Medicine, Nursing and Health Sciences
Organisational Unit	Department of Epidemiology and Preventive Medicine
Offered	Not offered in 2015
Coordinator(s)	Associate Professor Lyle Gurrin

Synopsis

This unit provides a thorough introduction to the concepts and methods of modern Bayesian statistical methods with particular emphasis on practical applications in biostatistics. Comparison of Bayesian concepts involving prior distributions with classical approaches to statistical analysis, particularly likelihood based methods. Applications to fitting hierarchical models to complex data structures via simulation from posterior distributions using Markov chain Monte Carlo techniques (MCMC) with the WinBUGS software package.

Outcomes

Upon successful completion of this unit, students should be able to:

Explain the logic of Bayesian statistical inference i.e. the use of full probability models to quantify uncertainty in statistical conclusions.
Develop and analytically describe simple one-parameter models with conjugate prior distributions and standard models containing two or more parameters including specifics for the normal location-scale model.
Appreciate the role prior distributions and have a thorough understanding of the connection between Bayesian methods and standard 'classical' approaches to statistics, especially those based on likelihood methods.
Recognise situations where a complex biostatistical data structure can be expressed as a Bayesian hierarchical model, and specify the technical details of such a model.
Explain and use the most common computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions based on Markov Chain Monte Carlo (MCMC) methods, with emphasis on the practical implementation of such techniques in the WinBUGS package.
Perform practical Bayesian analysis relating to health research problems, and effectively communicate the results.