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Bayesian Statistical Methods (POPH90139)
Graduate courseworkPoints: 12.5Not available in 2019
Overview
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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: 3 November 2022
Eligibility and requirements
Prerequisites
- POPH90014 Epidemiology 1 OR POPH90016 Epidemiology
- POPH90148 Probability and Distribution Theory
- MAST90100 Inference Methods in Biostatistics OR POPH90017 Principles of Statistical Inference
- MAST90102 Linear Regression OR POPH90120 Linear Models
- MAST 90099 Categorical Data: Models and Methods OR POPH90121 Categorical Data & GLMs
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
Two written assignments to be submitted during semester worth 30% each (approx 10 hrs work each).
Four practical exercises to be submitted during semester worth 10% each (approx 6 hrs work each).
Last updated: 3 November 2022
Dates & times
Not available in 2019
Time commitment details
170 hours
Additional delivery details
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
Gelman, A, Carlin, JB, Stern, HS, and Rubin, DB, Bayesian Data Analysis, 2nd edition, Chapman and Hall, 2003. ISBN 158488388X
Special Computer Requirements: Subject coordinator will advise (no licensing costs involved)
Resources Provided to Students: Printed course notes, including published literature, and assignment material by mail and email, and online interaction facilities.Recommended texts and other resources
None
- Subject notes
This subject is not available in the Master of Public Health.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Graduate Diploma in Biostatistics Course Master of Biostatistics - Links to additional information
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022