Postgraduate SWI-STA80007-2018

Bayesian Statistics

Calculate and apply your understanding of Bayesian statistical modelling. Figure out a sum of estimation techniques. Account for estimation differences. Assess the importance of Markov Chain Monte Carlo simulation in Bayesian analysis.

Duration

13 weeks

Student rating

3.5

Study method

100% online

Available loans

FEE-HELP

Assessments

Subjects may require attendance

Prior study

Required

Study terms

26 Aug 2019

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University ranking

Subject details

What you'll learn

Students who successfully complete this subject will be able to:

Differentiate important distributions commonly used in Bayesian Statistic
Defend the importance of concepts such as Prior Distributions and Posterior Distributions in Bayesian Statistical Modeling
Describe the importance of Markov Chain Monte Carlo simulation in Bayesian Analysis
Develop programming capabilities to perform Bayesian analysis
Evaluate empirical applications of Bayesian analysis in an appropriate software environment
Articulate the differences between Bayesian estimation and maximum likelihood estimation
Argue the merits of Bayesian methodology.

Topics covered

- Bayes theorem and the concept of Bayesian statistics
- Distributions often used in Bayesian statistics
- Prior distributions, likelihood functions, and posterior distributions
- Bayesian estimation and model fitting using appropriate software
- Differences between Bayesian estimation and maximum likelihood estimation
- Choice of prior distribution
- Empirical Bayes estimation
- Bayesian hierarchical models
- Empirical applications of Bayesian analysis using appropriate software
Study resources
- Instructional methods
  - Discussion forum/Discussion Board
  - Online Quizzes/Tests
  - Online assignment submission
  - Podcasting/Lecture capture
  - Standard Media
  - Web links
- Print materials
  - Welcome letter

Entry requirements

You must have successfully completed the following subject(s) before starting this subject:

SWI-STA60003-Basic Statistical Computing Using R , or SWI-HMS772

Special requirements

No special requirements

Description

The subject introduces the fundamentals of Bayesian statistical modelling. Students will learn the importance of subjective beliefs in Bayesian statistics. Important concepts such as prior distributions, likelihood functions, and posterior distributions will be discussed at length. Numerical estimation techniques, such as Metropolis-Hastings and Gibbs sampling, will be introduced. Empirical applications of Bayesian analysis will be performed in an R software environment.

Please note: assessment values are indicative only, details will be advised at the start of the subject.

Assessments

Assignments — 2 (40%)
Invigilated Exam (50%)
Quizzes — Online (10%)

Textbooks

Current study term (25 Aug 19 to 24 Nov 19)

Click on the titles of the listed books below to find out more:

Applied Bayesian Statistics: With R and OpenBUGS Examples

Mary Kathryn Cowles

Required

Related degrees

postgraduate SWI-STA-MAS-2018

Master of Applied Statistics