STATS 331 : Introduction to Bayesian Statistics


2024 Semester Two (1245) (15 POINTS)

Course Prescription

Introduces Bayesian data analysis using the WinBUGS software package and R. Topics include the Bayesian paradigm, hypothesis testing, point and interval estimates, graphical models, simulation and Bayesian inference, diagnosing MCMC, model checking and selection, ANOVA, regression, GLMs, hierarchical models and time series. Classical and Bayesian methods and interpretations are compared.

Course Overview

This course introduces statistics students to the Bayesian framework for performing inference. This framework is logical and consistent, and provides students with a unified view of statistics, replacing traditional approaches to hypothesis testing and parameter estimation. The course starts with a review of probability theory and, Bayesian inference is introduced with simple toy examples to demonstrate the basic principles. The problems gradually build in complexity. In the middle of the course, Markov Chain Monte Carlo (MCMC) methods are introduced. A basic implementation of the Metropolis algorithm is presented, but most of the data analysis applications are performed using the JAGS sampler, run from within R. The latter half of the course addresses some standard data analysis situations from the Bayesian point of view using JAGS, including univariate distributions, regression, time series, and hierarchical models.  Along with the labs, these provide students with experience implementing and interpreting Bayesian analyses in common situations.  The course enables a student to perform the kind of analyses encountered in STATS 201/7/8 from a Bayesian perspective and leads to STATS 731. 

Course Requirements

Prerequisite: ENGSCI 314 or STATS 201 or 208

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Describe the Bayesian conceptions of probability, and explain how it is used for inference. (Capability 3, 4, 5 and 6)
  2. Perform parameter estimation in R for some standard problems. (Capability 3, 4 and 5)
  3. Perform basic model selection in R for some standard problems. (Capability 3, 4 and 5)
  4. Analytically calculate the posterior distribution for a single parameter in some simple cases involving conjugate priors. (Capability 3, 4 and 5)
  5. Describe the Metropolis algorithm. (Capability 3, 4 and 5)
  6. Use and interpret the output of inferences computed using JAGS from within R, in a range of situations. (Capability 3, 4, 5 and 6)


Assessment Type Percentage Classification
Assignments 24% Individual Coursework
Quizzes 6% Individual Coursework
Test 20% Individual Test
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Final Exam
Students must score more than 45% in the final exam. 


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Special Requirements

The test will be held during the evening.

Workload Expectations

This course is a standard 15-point course and students are expected to spend 150 hours per semester involved in each 15-point course that they are enrolled in. 

For this course, a typical weekly workload includes:

  • 2 hours of lectures
  • 1-hour tutorial
  • 4 hours of reviewing the course content and thinking about the content
  • 3 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including labs to complete components of the course.
Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
Attendance in person will be required for any on campus test and exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

All the necessary material will be on Canvas.

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.


Other Information

R software will be used.

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


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.

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

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.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 06/11/2023 08:40 a.m.