Module manager: TBC
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Taught: Semester 1 Jul to 31 Aug View Timetable
Year running 2024/25
N/A
| OMAT5201M | Linear Modelling |
| OMAT5203M | Statistical Learning |
| OMAT5205M | Multivariate Methods |
N/A
This module is not approved as an Elective
The use of computers in mathematics and statistics provides a wide range of techniques for studying otherwise intractable problems and for analysing very large data sets. "Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model. This module gives an overview of key methods in statistical computing. One of the most important ideas in statistical computing is, that often properties of a stochastic model can be found experimentally, by using a computer to generate many random instances of the model, and then statistically analysing the resulting sample. The resulting methods are called Monte Carlo methods, and discussion of such methods forms the main focus of this module.
The module aims to equip students with the ability to apply standard methods for random number generation and apply different Monte Carlo methods and develop understanding of the principles and methods of stochastic simulation. The module will also instruct students on how to implement statistical algorithms for a given problem and develop familiarity with software for advanced statistical computing.
On completion of this module students should be able to:
1. Be aware of how computers generate random numbers using different methods.
2. Understand and implement Monte-Carlo methods.
3. Understand and implement Markov Chain Monte Carlo (MCMC) methods.
4. Implement resampling methods.
Skills developed in this module include:
- computing and programming skills;
- evaluating the quality of data and selecting suitable methods of analysis;
- fitting models to data and interpreting the results;
- communicating the outcome of data analysis.
Indicative content for this module includes:
1. Random number generation
2. Monte-Carlo methods
3. Markov Chain Monte Carlo (MCMC) methods
4. Resampling methods
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 1 | 1.5 | 1.5 |
| On-line Learning | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | MCQ test and short answer questions | 20 |
| Assignment | Project Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.
There is no reading list for this module
Last updated: 5/30/2024
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