Course Title: Applied Bayesian Statistics

Part A: Course Overview

Course Title: Applied Bayesian Statistics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2305

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2016

MATH2305

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2018,
Sem 2 2020,
Sem 2 2022,
Sem 2 2024

Course Coordinator: Dr. Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

Course Coordinator Email: haydar.demirhan@rmit.edu.au

Course Coordinator Location: 15.04.15

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study

It is recommended to have satisfactorily completed the following course/s before you commence this course:

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.

Assumed Knowledge

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 program learning outcomes for the following program(s): 

BP350 - Bachelor of Science (Statistics Major)

PLO 1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines.
PLO 2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities.
PLO 3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making.
PLO 4 Communicate, report and reflect on scientific findings, to diverse audiences utilising a variety of formats employing integrity and culturally safe practices.

BP083P23 - Bachelor of Applied Mathematics and Statistics (Statistics Major)

PLO 1 Apply a broad and coherent knowledge of mathematical and statistical theories, principles, concepts and practices with multi-disciplinary collaboration.
PLO 2 Analyse and critically examine the validity of mathematical and statistical arguments and evidence using methods, technical skills, tools and computational technologies.
PLO 3 Formulate and model real world problems using principles of mathematical and statistical inquiry to inform evidence-based decision making.
PLO 4 Critically evaluate and communicate technical and non- technical mathematical and statistical knowledge to diverse audiences utilising a variety of formats employing culturally safe practices.

For more information on the program learning outcomes for your program, please see the program guide.  


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.

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Assessment Tasks

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

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

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

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.