| Unit name | Bayesian Modelling B |
|---|---|
| Unit code | MATH34920 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Yu |
| Open unit status | Not open |
| Pre-requisites | |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
This unit will develop on the material covered in Bayesian Modelling A, and will provide the necessary background, experience and modern computational tools to apply Bayesian modelling techniques to realistic applications. The course will start with a gentle introduction to the basic principles of Monte Carlo techniques, with examples of applications in science. The elegance, simplicity and power of the concepts will motivate their use in the context of Bayesian inference. The course will then focus on Markov Chain Monte Carlo and Sequential Monte Carlo techniques. These methods have revolutionised statistical inference over the last 10 - 15 years. The application of these powerful tools will be gradually introduced and illustrated with practical examples from various fields of science, including finance, telecommunications, biology, and nuclear science.
