Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Understand how to approximate a distribution with a known probability distribution
- Understand how to numerically maximise a likelihood function
- Understand how to sample from an arbitrary distribution
Syllabus
Revision of inference basics
Numerical optimisation (Netwon-Raphson, Fisher scoring, EM algorithm)
Resampling methods (bootstrap, jacknife)
Sampling distributions (e,g. MCMC)
Deterministic approximations to distributions (e.g. Laplace, VB)
Learning and Teaching
Teaching and learning methods
Lectures, problem classes, computer labs
| Type | Hours |
|---|---|
| Teaching | 48 |
| Independent Study | 102 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Exam | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |