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| Postgraduate |
(MED)
|
Leader: Dr Lyle Gurrin
Offered:
Clayton First semester 2005 (OCL)
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.
Objectives: On completion of this unit students should be able to: 1. Explain the logic of Bayesian statistical inference i.e. the use of full probability models to quantify uncertaintly in statistical conclusions. 2. 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. 3. 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. 4. 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. 5. 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. 6. Perform practical Bayesian analysis relating to health research problems, and effectively communicate the results.
Assessment: The assessment for this unit will involve two major written assignments (each worth 40%) and submission of selected practical exercises (20%). The assignments will each involve a case study in which a biomedical scenario, research questions and a dataset will be provided to the students.
Prerequisites: MPH1040 Epidemiology; EPM5002 Mathematical Background for Biostatistics; EPM5003 Principles of Statistical Inference; EPM5004 Linear Models; EPM5009 Categorical Data Analysis and Generalised Linear Models