Outline

Overview

The aim of this unit is 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. This unit covers: 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; application of Bayesian methods for fitting hierarchical models to complex data structures. R will be used for simulations and model fitting using MCMC routines.

Unit details and rules

Unit code	BSTA5014
Academic unit	Public Health
Credit points	6
Prohibitions ?	None
Prerequisites ?	(PUBH5010 or BSTA5011 or CEPI5100) and BSTA5002 and (BSTA5210 or (BSTA5007 and BSTA5008))
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Module 1 Exercise Short written assignment based on Module 1 Exercises.	10%	- Due date: 15 Aug 2022 at 23:59	500 words
Outcomes assessed:
Assignment	Module 2 Exercise Short written assignment based on Module 2 Exercsies	10%	- Due date: 29 Aug 2022 at 23:59	500 words
Outcomes assessed:
Assignment	Major Assignment 1 Major written assignment	30%	- Due date: 12 Sep 2022 at 23:59	1500 words
Outcomes assessed:
Assignment	Module 4 Exercise Short written assignment based on Module 4 Exercises.	10%	- Due date: 26 Sep 2022 at 23:59	500 words
Outcomes assessed:
Assignment	Module 5 Exercise Short written assignment based on Module 5 Exercises.	10%	- Due date: 17 Oct 2022 at 23:59	500 words
Outcomes assessed:
Assignment	Major Assignment 2 Major written assignment	30%	- Due date: 11 Nov 2022 at 23:59	1500 words
Outcomes assessed:

Assessment summary

Four short written assignments (module exercises) for Modules 1,2,4 and 5.
Two 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.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Module 1: Bayesian Concepts	Independent study (20 hr)
	Module 2: Continuous priors - conjugate distributions	Independent study (20 hr)
	Module 3: MCMC fitting and assessing convergence	Independent study (20 hr)
	Module 4: Model evaluation and comparison	Independent study (20 hr)
	Module 5: Hierarchical models and shrinkage	Independent study (20 hr)
	Module 6: Bayesian workflow and open problems	Independent study (10 hr)
Week 01	Introduction / R Refresher	Independent study (10 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 textbook is recommended (not compulsary):

Lambert B. (2018) A Student’s Guide to Bayesian Statistics. SAGE Publications Ltd. ISBN: 9781473916364.

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, 2020. In response to student feedback, the unit will have a substantial overhaul, particularly to the subject notes. Some of the longer derivations and obsolete software references have been removed, with a greater focus on coding required in the first instance. Additional structure has been introduced for each module (comprising a worked example, classroom example, and a self-test per module), and hierarchical modules are postponed until later in the unit. Non-assessed quizzes will also be introduced.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

BSTA5014: Bayesian Statistical Methods

Semester 2, 2022 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect