# Bayesian Statistics - 2016

To enrol in this unit, you must be accepted into a course from the provider. Read before you start

## Unit summary

### STA80007

• Delivery method: Fully Online
• Prerequisites: Yes
• Duration: 13 weeks
• Government loans available: FEE-HELP
• Availability for 2016: SP3

Unit provided by

2016 Fees
AUD\$
Domestic 2,600.00
Domestic continuing 2,332.00
International 2,850.00
International continuing 2,582.00

Tuition fees are reduced for continuing registered SWI students.

The aim of this unit is to introduce 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.

On successful completion of this unit students will be able to:
1. Differentiate important distributions commonly used in Bayesian Statistics
2. Defend the importance of concepts such as Prior Distributions and Posterior Distributions in Bayesian Statistical Modeling
3. Describe the importance of Markov Chain Monte Carlo simulation in Bayesian Analysis
4. Develop programming capabilities to perform Bayesian analysis
5. Evaluate empirical applications of Bayesian analysis in an R software environment
6. Articulate the differences between Bayesian estimation and maximum likelihood estimation
7. Argue the merits of Bayesian methodology using Markov Chain Monte Carlo simulation
• Assignments (40%)
• Invigilated Exam (50%)
• Quizzes (10%)

#### Mandatory prerequisites

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

In order to enrol in this unit, you must be accepted into one of the following courses:

This unit addresses the following topics.

NumberTopic
1An Introduction to Bayesian Statistics
2Bayesian Statistics using R packages
3Prior distributions, likelihood functions, and posterior distributions
4Common Distributions used in Bayesian Statistics
5Differences between Bayesian estimation and maximum likelihood estimation.
6Empirical applications of Bayesian analysis using R.
7Empirical applications of Bayesian analysis using R.
8Applying Bayesian methodology using Markov Chain Monte Carlo simulation.

This unit is delivered using the following methods and materials:

#### Instructional Methods

• Discussion Forum/Discussion Board
• Online Quizzes/Tests
• Online assignment submission
• Podcasting/Lecture capture
• Standard Media

#### Print based materials

• Welcome Letter

This unit is a core requirement in the following courses:

This unit may be eligible for credit towards other courses:

1. Many undergraduate courses on offer through OUA include 'open elective' where any OUA unit can be credited to the course. You need to check the Award Requirements on the course page for the number of allowed open electives and any level limitations.
2. In other cases, the content of this unit might be relevant to a course on offer through OUA or elsewhere. In order to receive credit for this unit in the course you will need to supply the provider institution with a copy of the Unit Profile in the approved format, which you can download here. Note that the Unit Profile is set at the start of the year, and if textbooks change this may not match the Co-Op textbook list.

Textbook information for this unit is currently being updated and will be available soon. Please check back regularly for updates. Alternatively, visit the The Co-op website and enter the unit details to search for available textbooks.

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