| Unit name | Bayesian Modelling B 34 |
|---|---|
| Unit code | MATHM4920 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Yu |
| Open unit status | Not open |
| Pre-requisites |
Applied Probability 2 (MATH 21400), Bayesian Modelling A (MATH 34910) |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Much of the real advantage of the Bayesian approach to statistical modelling and inference is only seen when dealing with the slightly more complex situations encountered in this unit. Hierarchical models allow us to model situations where we simultaneously analyse different groups of data and where the parameters describing the groups can be assumed to be similar, using 'graphical keep track of the different kinds of variation. We will discuss how to draw inference in such models, and then how to actually do that in practice, leading to discussion of Markov chain Monte Carlo (MCMC) techniques, which are powerful and elegant algorithms based on simple ideas of conditional probability. Graphical modelling and MCMC are the basis for a package called WinBugs for doing Bayesian analysis without needing to write your own program, and there will be demonstrations and some hands-on practice with using that package on range of interesting examples.
