Applied Bayesian Methods

Description

The course introduces students to Bayesian thinking and methods from an applied point of view; covering the use of prior information, Bayes' rule and inference in standard situations such as proportions, means and relationships between variables. An applied view on Markov chain Monte Carlo methods will also be given. These methods are becoming popular among applied statisticians and analysts from disciplines such as, Economics, Quantitative finance, Health, Environmental science, Engineering and other applied areas, especially because prior information can be incorporated directly into analyses in a sensible way.

This course is open to students in the BMath program (including double degree programs) or to students in other programs who have received explicit permission from the Head of Discipline of Statistics.

This course is shared by the Universities of Newcastle, Western Sydney and Wollongong as part of the Applied Statistics Education and Research Collaboration (ASEARC). In some years, the course will be beamed live from one of the other institutional partners using the Access Grid Room rather than being taught face-to-face at Newcastle.

Availability

Callaghan Campus

  • Semester 2 - 2015

Learning Outcomes

1. Understand Bayesian thinking;

2. Use prior information and Bayes' rule in probability and statistical inference problems;

3. Apply Bayesian inference methods to common parameters (binomial, Normal) and to relationships between variables; and

4. Compare these with frequentist methods.

Content

Introduction to Bayesian thinking

The use of prior information

Bayesian estimation of:

  • the binomial parameter
  • the Normal mean and variance
  • the poisson parameter

Empirical Bayes estimation

Bayesian estimation in:

  • analysis of variance
  • regression

Markov chain Monte Carlo methods

Requisites

This course is open to students in other programs who have received explicit permission from the Head of Discipline of Statistics.

Assumed Knowledge

STAT2010

Assessment Items

Written Assignment: Assignments (x3)

Formal Examination: Final exam

Contact Hours

Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term

Lecture

Face to Face On Campus 2 hour(s) per Week for Full Term