Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Implement Monte Carlo integration and understand when it should be used
- Implement bootstrapping and understand when it should be used
- Implement basic random number generation
- Implement Markov chain Monte Carlo (MCMC) algorithms and understand when they should be used
Syllabus
- Introduction: random number generation and optimisation
- Monte Carlo sampling and integration
- Rejection and importance sampling
- Markov chain Monte Carlo for posterior sampling
- Computational frequentist inference via bootstrapping
Learning and Teaching
Teaching and learning methods
Independent study
| Type | Hours |
|---|---|
| Teaching | 24 |
| Independent Study | 51 |
| Total study time | 75 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 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 |
|---|---|
| Coursework | 100% |