Course Title: Applied Bayesian Statistics

Part A: Course Overview

Course Title: Applied Bayesian Statistics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2016


City Campus


171H School of Science


Sem 2 2018,
Sem 2 2020,
Sem 2 2022

Course Coordinator: Dr. Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

Course Coordinator Email:

Course Coordinator Location: 15.04.15

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics

This course assumes a background level knowledge of R Software 

Course Description

This course will provide you with an introduction to the Bayesian framework in statistics, including the differences between Bayesian approaches and the more standard frequentist approach.  It will cover basic single and multi-parameter models, regression, Bayesian estimation (including MCMC simulation techniques), and hierarchical models.  You will be introduced into Bayesian modelling through the use of the R programming language and JAGS as computational tools.  The emphasis in the course is in encouraging you to apply Bayesian methods in various environments including business and health sciences.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP245 Bachelor of Science (Statistics) and BH119 Bachelor of Analytics (Honours):

Knowledge and technical competence

  • An understanding of relevant, applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • An understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution

Upon successful completion of this course you should be able to:

  1. Independently formulate Bayesian models for analysing datasets arising from non-trivial statistical designs and observational data settings.
  2. Conduct Bayesian statistical analyses demonstrating familiarity with the R programming language and JAGS.
  3. Produce comprehensive oral and written reports of Bayesian statistical analyses.

Overview of Learning Activities

Learning activities of the course include lectures and practice sessions where you will apply the methodology covered in the lectures. Class time will be divided into two parts. In the first part, methodological aspects of Bayesian statistics and models will be illustrated with facilitated demonstrations, and then, students will apply the methodology over the real datasets and discuss analysis results to foster their understanding. Students will work with JAGS to implement Markov chain Monte Carlo methods in small groups or pairs in the practice sessions.

The main focus of the course will be on the implementation of mainstream Bayesian analysis methods and their implementation on R software. The contents will be explained with examples and online demonstrations in lectures. Because R software will be used for all analyses, a good knowledge of R is essential for this course. Practice sessions, assignments, and a project assignment will provide an opportunity to carry out analyses following a structured format and test your understanding of the topics covered in classes.


Total Study Hours

You will undertake 4 hours per week of face-to-face learning through lecture sessions. Meanwhile an average of 6 hours/week of independent study is recommended.

Overview of Learning Resources

All learning resources including lecture notes, lab/practical materials, etc. will be provided on Blackboard via myRMIT.  A detailed list of references will be provided during the semester the course is offered.

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

Assessment Tasks:

Assessment 1: Assignment Report 1
Weighting 25%
This assessment task supports all CLOs 1-3

Assessment 2: Assignment Report 2
Weighting 25%
This assessment task supports all CLOs 1-3

Assessment 3: Final Project
Weighting 50%
This assessment task supports all CLOs 1-3