• Class Number 8374
  • Term Code 2960
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Bronwyn Loong
    • Dr Bronwyn Loong
  • Class Dates
  • Class Start Date 22/07/2019
  • Class End Date 25/10/2019
  • Census Date 31/08/2019
  • Last Date to Enrol 29/07/2019
    • Dehua Tao
SELT Survey Results

The Bayesian approach to statistics assigns probability distributions to both the data and unknown parameters in the problem.  This way, we can incorporate prior knowledge on the unknown parameters before observing any data.  Statistical inference is summarised by the posterior distribution of the parameters after data collection, and posterior predictions for new observations.  The Bayesian approach to statistics is very flexible because we can describe the probability distribution of any function of the unknown parameters in the model.  Modern advances in computing have allowed many complicated models, which are difficult to analyse using ‘classical’ (frequentist) methods, to be readily analysed using Bayesian methodology.  

The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.  The first part of the course is devoted to describing the fundamentals of Bayesian inference by examining some simple Bayesian models.  More complicated models will then be explored, including linear regression and hierarchical models in a Bayesian framework.  Bayesian computational methods, especially Markov Chain Monte Carlo methods will progressively be introduced as motivated by the models discussed.   Emphasis will also be placed on model checking and evaluation.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

Upon successful completion of the requirements for this course, students should have the knowledge and skills to:

  1. Explain the Bayesian framework for data analysis and its flexibility in contrast to the frequentist approach; appreciate when the Bayesian approach can be beneficial.
  2. Develop, analytically describe, and implement common probability models (both single and multiparameter) in the Bayesian framework (this includes models for regression analysis and generalised linear models).
  3. Appreciate the role of the prior distribution in Bayesian inference, and in particular the usage of non-informative priors and conjugate priors.
  4. Interpret the results of a Bayesian analysis and perform Bayesian model evaluation and assessment.
  5. Recognise the need to fit hierarchical models and provide the technical specifications for such models.
  6. Perform Bayesian computation using Markov chain Monte Carlo methods using R.
  7. Formulate a Bayesian solution to real-data problems.
  8. Communicate complex statistical ideas to a diverse audience.
  9. Demonstrate the necessary research skills to form a hypothesis, collect and analyse data, and reach appropriate conclusions.

Research-Led Teaching

Throughout the course, relevant journal articles may be discussed as supplementary material.

The final project will involve the application of methodology learned in the course to a real data

set. Students will be required to formulate their own research questions, select and implement the

appropriate statistical model(s), and write a report to communicate their findings.

Field Trips

Not relevant

Required Resources


”A First Course in Bayesian Statistical Methods”, Hoff, P. (2009). Springer: New York. (available on eReserve at the library)

Technology, Software, Equipment:

You will be expected to perform data analyses using statistical software as part of your coursework. The official computer package for this course is R, which runs on Windows, MacOS and UNIX platforms. The software is free and available online through www.rproject.org: It is assumed students have a working knowledge of R from the pre-requisite course STAT2008. The use of other statistical programs is permitted but support will be provided solely for R.

  1. ”Bayesian Data Analysis”. Gelman, A., Carlin, JB., Stern, HS., Dunson, DB., Vehtari, A., and Rubin, DB. (third edition) (2014). CRC Press: Florida. (available on short term loan reserve at Hancock library)
  2. “Bayesian methods for data analysis”, Carlin, BP. and Louis, TB. (third edition) (2009). CRC Press: Florida. (available on short term loan reserve at Hancock library)
  3. “Introduction to Bayesian statistics”, Bolstad, WM. (2004). Wiley: New Jersey. (available online at ANU library)
  4. “Applied Bayesian Modelling”, Congdon, P (2014). (second edition). Wiley: New Jersey. (availableonline at ANU library)
  5. “Bayesian ideas and data analysis : an introduction for scientists and statisticians”, Christensen, R. et al. (2011). CRC Press: Floriday (available on short term loan reserve at Hancock library)

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

As a further academic integrity control, students may be selected for a 15 minute individual oral examination of their written assessment submissions.

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.”

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to Bayesian inference; Review of probability (Hoff Chapters 1 and 2)
2 Bayesian inference for one parameter models (Hoff Chapter 3)
3 Bayesian inference for one parameter models (Hoff Chapter 3); Monte Carlo approximation and model checking (Hoff Chapter 4)
4 Bayesian inference for the normal model (Hoff Chapter 5) Assignment 1 Due
5 Gibbs sampling and MCMC convergence diagnostics (Hoff Chapter 6)
6 Multivariate Normal Distribution (Hoff Chapter 7) In- Class Test
7 Hierarchical Models (Hoff Chapter 8)
8 Bayesian Linear Regression (Hoff Chapter 9 ) Assignment 2 Due
9 Metropolis-Hastings Algorithm (Hoff Chapter 10)
10 Mixed effects models (Hoff Chapter 11)
11 Latent variable methods for ordinal data (Hoff Chapter 12); Bayesian models for missing data Assignment 3 Due
12 Further topics in Bayesian Computation - computationally efficient MCMC (Variational Bayes, Hamilton Monte Carlo, Adaptive MCMC); Introduction to Bayesian Nonparametric models Final Project Due in Exam Period

Tutorial Registration

Please see Wattle for tutors’ information. Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle. When tutorials are available for enrolment, follow these steps:

1. Log on to Wattle, and go to the course site.

2. Click on the link “Tutorial enrolment”

3. On the right of the screen, click on the tab “Become Member of ……” for the tutorial class you wish to enter.

4. Confirm your choice

If you need to change your enrolment, you will be able to do so by clicking on the tab “Leave group…” and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.”

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 10 % 15/08/2019 22/08/2019 LO1, LO2, LO3
In-class test 10 % 28/08/2019 04/09/2019 LO1, LO2, LO3, LO4
Assignment 2 10 % 26/09/2019 03/10/2019 LO4, LO5, LO6
Assignment 3 10 % 17/10/2019 24/10/2019 LO4, LO5, LO6, LO7
Final Project 60 % 01/11/2019 15/11/2019 LO3 - LO9

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 10 %
Due Date: 15/08/2019
Return of Assessment: 22/08/2019
Learning Outcomes: LO1, LO2, LO3

Assignment 1

Assignment 1 will require students to implement simple Bayesian models as discussed in class using a statistical software package. Algebraic derivations, exploration of theoretical topics and explanation of theoretical results and concepts may also be required. Assignment 1 will be made available by the end of Week 1.

Assignment 1 is mandatory and individual-based

Assessment Task 2

Value: 10 %
Due Date: 28/08/2019
Return of Assessment: 04/09/2019
Learning Outcomes: LO1, LO2, LO3, LO4

In-class test

The in-class test will examine your understanding of basic Bayesian concepts. In particular, specification of a posterior distribution given a prior and likelihood function. Algebraic derivations, explanation of theoretical concepts, and interpretation of analytical results will be required.

The in-class test is mandatory and will be open book.

Assessment Task 3

Value: 10 %
Due Date: 26/09/2019
Return of Assessment: 03/10/2019
Learning Outcomes: LO4, LO5, LO6

Assignment 2

Assignment 2 will require students to fit more complicated Bayesian models (example, multivariate normal and hierarchical Bayesian models) and implement the Gibbs sampling algorithm using a statistical software package. Algebraic derivations, exploration of theoretical topics and explanation of theoretical results and concepts may also be required. Assignment 2 will be made available by the end of Week 6.

Assignment 2 is mandatory and individual-based

Assessment Task 4

Value: 10 %
Due Date: 17/10/2019
Return of Assessment: 24/10/2019
Learning Outcomes: LO4, LO5, LO6, LO7

Assignment 3

Assignment 3 will require students to fit Bayesian regression models and implement the Metropolis-Hastings sampling algorithm using a statistical software package. Algebraic derivations, exploration of theoretical topics and explanation of theoretical results and concepts may also be required. Assignment 3 will be made available by the end of Week 8.

Assignment 3 is mandatory and individual-based

Assessment Task 5

Value: 60 %
Due Date: 01/11/2019
Return of Assessment: 15/11/2019
Learning Outcomes: LO3 - LO9

Final Project

The final project will involve application of material learned in the course to a real data set. Students may analyse a data set of their own choice (subject to lecturer approval)

or choose one of the data sets provided by the lecturer to analyse. Students will be required to formulate their own research question and demonstrate application of statistical methodology learned in STAT3016. Findings are to be communicated in a written report. Further instructions and grading guidelines will be provided later. The final project instructions will be made available by the end of Week 4.

The final project is mandatory and individual-based

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education), submission of all assignments and reports must be submitted electronically through Turnitin.

Hardcopy Submission

All assignments are to be submitted electronically via Turnitin.

Late Submission

Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

via Turnitin

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Resubmission of assignments is not allowed after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Bronwyn Loong
6125 7312

Research Interests

Bayesian analysis, missing data, data confidentiality

Dr Bronwyn Loong

Wednesday 13:00 15:00
Wednesday 13:00 15:00
Dr Bronwyn Loong
6125 7312

Research Interests

Dr Bronwyn Loong

Wednesday 13:00 15:00
Wednesday 13:00 15:00
Dehua Tao

Research Interests

Dehua Tao

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions