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Unit outline_

BSTA5014: Bayesian Statistical Methods

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

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

Academic unit Public Health
Credit points 6
Prerequisites
? 
(PUBH5010 or BSTA5011 or CEPI5100) and BSTA5002 and (BSTA5210 or (BSTA5007 and BSTA5008))
Corequisites
? 
None
Prohibitions
? 
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: LO1 LO2 LO3
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: LO1 LO2 LO3
Assignment Major Assignment 1
Major written assignment
30% -
Due date: 12 Sep 2022 at 23:59
1500 words
Outcomes assessed: LO1 LO5 LO3 LO2
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: LO3 LO4
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: LO3 LO4
Assignment Major Assignment 2
Major written assignment
30% -
Due date: 11 Nov 2022 at 23:59
1500 words
Outcomes assessed: LO3 LO4 LO5

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
Multiple weeks Module 1: Bayesian Concepts Independent study (20 hr) LO1 LO2 LO3
Module 2: Continuous priors - conjugate distributions Independent study (20 hr) LO1 LO2 LO3
Module 3: MCMC fitting and assessing convergence Independent study (20 hr) LO2 LO3 LO4
Module 4: Model evaluation and comparison Independent study (20 hr) LO3 LO4
Module 5: Hierarchical models and shrinkage Independent study (20 hr) LO3 LO4
Module 6: Bayesian workflow and open problems Independent study (10 hr) LO4 LO5
Week 01 Introduction / R Refresher Independent study (10 hr) LO4

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.

This unit is externally delivered as part of the 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.