Bayesian Statistical Methods (POPH90139)
Graduate courseworkPoints: 12.5Online
About this subject
Contact information
Semester 2
Overview
| Availability | Semester 2 - Online |
|---|---|
| Fees | Look up fees |
This subject introduces Bayesian statistical concepts and methods, with emphasis on practical applications in biostatistics. We begin with a discussion of subjective probability in quantifying uncertainty in the scientific process. Subsequently, the concept of full probability modelling is introduced and developed through single- and multi-parameter models with conjugate prior distributions. The connection with frequentist approaches is examined in light of the relationship between non-informative and informative prior distributions and their effect on posterior estimates We discuss the specification of appropriate prior distributions, including the concepts of non-informative and weakly informative priors. We consider the frequentist properties of Bayesian procedures. The application of Bayesian methods for fitting hierarchical models to correlated data structures is developed. Computational techniques for use in Bayesian statistics, especially the use of iterative simulation from posterior distributions using Markov chain Monte Carlo techniques (MCMC) will be covered using the Stan software through R.
Intended learning outcomes
On completion of this subject, students should be able to:
- Explain the difference between Bayesian and frequentist concepts of statistical inference.
- Demonstrate how to specify and fit simple Bayesian models with appropriate attention to the role of the prior distribution and the data model.
- Explain how these generative models can be used for inference, prediction and model criticism.
- Demonstrate proficiency in using statistical software packages (R and Stan) to specify and fit models, assess model fit, detect and remediate non-convergence, and compare models.
- Engage in specifying, checking and interpreting Bayesian statistical analyses in practical problems using effective communication with health and medical investigators.
Generic skills
- Independent problem solving;
- facility with abstract reasoning;
- clarity of written expression;
- sound communication of technical concepts.
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| POPH90014 | Epidemiology 1 |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90100 | Probability & Inference in Biostatistics | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90102 | Foundations of Regression | July (Dual-Delivery - Parkville) |
12.5 |
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90099 | Advanced Regression | September (Dual-Delivery - Parkville) |
12.5 |
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: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Four short assignments (500 words each) (10% each). Due in weeks 3, 5, 7, 9 and 11 respectively.
| Throughout the teaching period | 40% |
Major Assignment #1
| Week 8 | 30% |
Major Assignment #2
| During the examination period | 30% |
Last updated: 4 March 2025
Dates & times
- Semester 2 - Online
Coordinator John Carlin Mode of delivery Online Contact hours 2 hours per fortnight. Participate in online, live discussions moderated by the coordinator. Additional time will be required to complete the course material and assessments. Total time commitment 168 hours Teaching period 28 July 2025 to 26 October 2025 Last self-enrol date 8 August 2025 Census date 1 September 2025 Last date to withdraw without fail 26 September 2025 Assessment period ends 21 November 2025 Semester 2 contact information
Time commitment details
The subject is entirely online. Students are expected to (1) study subject materials and complete assessment tasks (12 hours per week) and (2) make regular posts to online discussions in response to topics and questions posed by the co-ordinator and other students (1 hour per week)
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Additional delivery details
Last updated: 4 March 2025
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 delivered online via our partners in the Biostatistics Collaboration of Australia (www.bca.edu.au). It is not generally available in the Master of Public Health nor in any program outside the MSPGH.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Biostatistics Course Graduate Diploma in Biostatistics - Links to additional information
Last updated: 4 March 2025