2024/25 Taught Postgraduate Module Catalogue

OMAT5300M Statistical Computing

15 creditsClass Size: 100

Module manager: TBC
Email: .

Taught: 1 Jul to 31 Aug View Timetable

Year running 2024/25

Pre-requisite qualifications

N/A

Pre-requisites

OMAT5201M	Linear Modelling
OMAT5203M	Statistical Learning
OMAT5205M	Multivariate Methods

Module replaces

N/A

This module is not approved as an Elective

Module summary

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.

Objectives

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.

Learning outcomes
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 outcomes
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.

Syllabus

Indicative content for this module includes:

1. Random number generation
2. Monte-Carlo methods
3. Markov Chain Monte Carlo (MCMC) methods
4. Resampling methods

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	1	1.50	1.50
On-line Learning	5	1.00	5.00
Discussion forum	6	2.00	12.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

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.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Online Assessment	MCQ test and short answer questions	20.00
Assignment	Project Report	80.00
Total percentage (Assessment Coursework)		100.00

Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.

Reading list

There is no reading list for this module

Last updated: 30/05/2024 14:20:01

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