STATS 731 : Bayesian Inference


2020 Semester One (1203) (15 POINTS)

Course Prescription

A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.

Course Overview

STATS 731 will consolidate the basic concepts of Bayesian inference covered in STATS 331, starting from first principles and building on the material in STATS 331 with major emphasis on more advanced Bayesian methods in applied data analysis.

Over the last decade, the Bayesian approach has revolutionised many areas of applied statistics such as biometrics, econometrics, market research, statistical ecology and physics. Its rise and enormous popularity today is due to the advances made in Bayesian computation through computer-intensive simulation methods. Knowledge of Bayesian procedures and software packages will become indispensable for any career in Statistics. We will be using the software package R and JAGS (or WinBUGS/OpenBUGS) for Bayesian computation, as well as some R programs.

This course will introduce the theory of Bayesian inference, computer-intensive simulation techniques for posterior computation, and put strong emphasis on modern, applied Bayesian data analysis. Topics covered include: the Bayesian approach, conjugate prior distributions, methods for specification of prior distributions, techniques for posterior computation (incl. Laplace approximations, simulation techniques, rejection sampling, Markov chain Monte Carlo methods, and Nested Sampling), Bayesian linear and nonlinear regression models, hierarchical models, dynamic models, approaches to model comparison and selection, and model criticism.

Course Requirements

Prerequisite: STATS 210 or 225

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand the Bayesian meanings of probability (Capability 1, 2, 3 and 4)
  2. Use the mathematics of probability theory competently (Capability 1, 2 and 3)
  3. Understand and describe the Bayesian approach to inference (Capability 1, 2, 3, 4 and 5)
  4. Perform applied Bayesian data analysis using a range of techniques (Capability 1, 2, 3, 4 and 5)


Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Tutorials 10% Individual Coursework
Midterm Test 20% Individual Test
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4
Midterm Test
Final Exam

50% required in the final exam to pass.

Learning Resources

Textbooks A course book will be provided. Furthermore, the following textbooks are recommended and available as e-books or on desk copy at Short Loans, Kate Edger Information Commons (Level 1):

Bayesian Computation with R by Jim Albert

Bayes and Empirical Bayes Methods for Data Analysis by Carlin and Louis

Bayesian Data Analysis by Gelman, Carlin, Stern, and Rubin

The BUGS Book by Lunn, Jackson, Best, Thomas, and Spiegelhalter

Bayesian Modeling Using WinBUGS by Ioannis Ntzoufras

Bayesian Statistics: An Introduction by Peter Lee

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect the following per week, on average: 2 hours of lectures, a 1 hour tutorial, 2 hours of reading and practicing the content and 5 hours of work on assignments and/or test/exam preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 20/12/2019 01:11 p.m.