Applied Bayesian Methods
|Course code STAT3120||Units 10||Level 3000||Faculty of Science and Information TechnologySchool of Mathematical and Physical Sciences|
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.
Available in 2015
|Objectives||On successful completion of this course, students will be able to:|
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
Markov chain Monte Carlo methods
|Modes of Delivery||External Mode|
|Contact Hours||Lecture: for 2 hour(s) per Week for Full Term|
Computer Lab: for 2 hour(s) per Week for Full Term
|Timetables||2015 Course Timetables for STAT3120|