units

EPM5013

Faculty of Medicine, Nursing and Health Sciences

Postgraduate - Unit

This unit entry is for students who completed this unit in 2014 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, or view unit timetables.

LevelPostgraduate
FacultyFaculty of Medicine, Nursing and Health Sciences
Organisational UnitDepartment of Epidemiology and Preventive Medicine
OfferedAlfred Hospital Second semester 2014 (Off-campus)
Coordinator(s)A/Prof 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

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 uncertainty 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

Written assignments (80%)
Practical exercises (20%).

Chief examiner(s)

Prerequisites

Co-requisites

Must be enrolled in course version {3420, 3421, 3422}

Prohibitions

This unit is only available to students enrolled in the Graduate Certificate, Graduate Diploma or Masters of Biostatistics.

Additional information on this unit is available from the faculty at: