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

Course Coordinator: Dr. Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

Course Coordinator Email:

Course Coordinator Location: 8.9.83

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324 Introduction to Statistics

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

On 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 will comprise the following:

  • Interactive lectures involving your participation and completion of required reading material prior to each lecture
  • Hands-on use of the R programming language performing Bayesian analysis
  • Active participation working with JAGS for implementing Markov Chain Monte Carlo (MCMC) simulations

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 Task 1: Assignments
Weighting 20%
This assessment task supports all CLOs 1-3

Assessment Task 2: Mid-semester test
Weighting 30%
This assessment task supports all CLOs 1-3

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