Bayesian Statistics
36 contact hours or online equivalent plus private study
One Study period
Overview
Students will learn to model and manage uncertainty and risk. Some of the most commonly used probability distributions will be introduced together with Monte Carlo simulations. The concept of Maximum Likelihood Estimation (MLE) will also be introduced, allowing the estimation of distribution parameters. Probabilistic methods and models will then be illustrated using appropriate examples and software.
Requisites
Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Learning outcomes
Students who successfully complete this unit will be able to:
- Identify appropriate probability distributions for modelling chance occurrences
- Simulate such distributions and obtain estimates of distribution parameters using the method of moments and maximum likelihood estimation
- Navigate probabilistic methods for working with uncertainty
- Formulate probabilistic models for risk in real world contexts
- Use probabilistic models to design statistical systems for the management of risk and uncertainty
Teaching methods
Hawthorn Online
Type | Hours per week | Number of weeks | Total (number of hours) |
---|---|---|---|
Online Directed Online Learning and Independent Learning | 12.50 | 12 weeks | 150 |
TOTAL | 150 |
Hawthorn
Type | Hours per week | Number of weeks | Total (number of hours) |
---|---|---|---|
Face to Face Contact (Phasing out) Seminar | 3.00 | 12 weeks | 36 |
Specified Learning Activities (Phasing out) Various | 2.00 | 12 weeks | 24 |
Unspecified Learning Activities (Phasing out) Independent Learning | 7.50 | 12 weeks | 90 |
TOTAL | 150 |
Assessment
Type | Task | Weighting | ULO's |
---|---|---|---|
Assignment 2 | Individual | 40% | 2,3,4 |
Examination | Individual | 50% | 1,4,5 |
Online Quizzes | Individual | 10% | 1,5 |
Content
- Introduction to commonly used probability distributions
- Introduction to Method of Moments and Maximum Likelihood Estimation (MLE) for estimation purposes
- Applications for discrete, continuous and mixture distributions
- Applications for univariate and multivariate distributions
- Development of statistical models for identifying risk factors
- Formulation of commonly used probabilistic models and systems for managing uncertainty (e.g. Acceptance Sampling, Process Control, Queuing Theory, Inventory Control, Reliability Theory, Markov Processes)
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.