Outline

Overview

The aim of this unit is to provide a solid understanding of how Bayesian methods are applied to solve data-related problems in medicine and health sciences. You will explore the differences between Bayesian and Frequentist (also known as classical or approximation-based) statistical approaches, and learn how to apply Bayesian methods. This unit covers: Bayesian model development using prior knowledge (single and multiple parameter models, such as generalised linear regression, mixed-models); the use of Directed acyclic graph (DAG) in the context of Bayesian hierarchical modelling; designing clinical trials and calculating sample sizes using Bayesian methods; and Bayesian model choice/selection, including computational techniques. The course will use R programming language and Stan, though no prior knowledge of Stan is required.

Unit details and rules

Academic unit	Public Health
Credit points	6
Prerequisites ?	BSTA5210 or BSTA5211 or (BSTA5007 and BSTA5008)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Shuvo Bakar, shuvo.bakar@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Major Assignment 3 Major written assignment	40%	STUVAC Due date: 07 Nov 2025 at 23:59	1500-2000 words	AI allowed
Outcomes assessed:
Written work	Major Assignment 1 Major written assignment	30%	Week 06 Due date: 05 Sep 2025 at 23:59	1500-2000 words	AI allowed
Outcomes assessed:
Written work	Major Assignment 2 Major written assignment	30%	Week 10 Due date: 06 Oct 2025 at 23:59	1500-2000 words	AI allowed
Outcomes assessed:

Assessment summary

Three major written assignments requiring the analysis, interpretation, and reporting / communicating of Bayesian analyses.
Non-assessed online quizzes will also be made available.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Grade	Mark Range	Description
AF Absent fail	Range from 0 to 49	To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level.
FA Fail	Range from 0 to less than 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard.
PS Pass	Range from 50 to less than 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard.
CR Credit	Range from 65 to less than 75	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard.
D Distinction	Range from 75 to less than 85	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard.
HD High distinction	Range from 85 to 100 inclusive	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 50%.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Module 1: Bayesian Dreams! (Navigating Evidence, Bayesian Inference)	Independent study (20 hr)
	Module 2: Chaotics? (Prior and Posterior, Generative Models and Tools)	Independent study (20 hr)
	Module 3: Bayeswatch! (Logical Connections, Prior Tweaks and More)	Independent study (20 hr)
	Module 4: Non-Gaussian Bayeswatch! (Non-Gaussian Models, More on Non-Gaussian)	Independent study (20 hr)
	Module 5: Clusterphobia? (Cluster Smart with Bayes, Goldilocks)	Independent study (20 hr)
	Module 6: Wander into the Wonder! (Size Matters, Discover the Perfect Spell)	Independent study (20 hr)
Week 01	Introduction / R Refresher	Independent study (5 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

The following textbooks are recommended (not compulsary):

Gelman A., et al. (2021). Bayesian Data Analysis (3rd edition)

McElreath R. (2020). Statistical Rehinking (2nd edition)

Lambert B. (2018). A Student's Guide to Bayesian Statistics

Kruschke J. (2015). Doing Bayesian Data Analysis (2nd edition)

Berry S.M., et al. (2010). Bayesian Adaptive Methods for Clinical Trials.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Explain the difference between Bayesian and frequentist concepts of statistical inference.
LO2. Demonstrate how to specify and fit simple Bayesian models with appropriate attention to the role of the prior distribution and the data model.
LO3. Explain how these generative models can be used for inference, prediction, and model criticism.
LO4. Demonstrate proficiency in using statistical software packages (R and Stan) to specify, fit, diagnose, and compare models.
LO5. Engage in specifying, checking, and interpreting Bayesian statistical analyses in practical problems using effective communication with health and medical investigators.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This unit was last offered in Semester 2, 2022. In response to student feedback, the unit will undergo a substantial overhaul, particularly in the subject notes and assignments. Some of the longer derivations and obsolete software references have been removed. There will be a greater focus on coding in R, initially using the "brms" package. Additional structure has been introduced to each module, comprising worked examples, classroom examples, and tests/quizzes for each module (not dependent on waiting for marks). Non-assessed quizzes will also be introduced during tutorials.

Additional information

This unit is externally delivered as part of the Biostatistics Collaboration of Australia (BCA).

Biostatistics Collaboration of Australia (BCA)

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

BSTA5014: Bayesian Statistical Methods (BAY)

Semester 2, 2025 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links