Total Credits: 20
Level: Level 4
Target Students: Single Honours students. MSc students. Available to JYA/Erasmus students.
|Full Year||Assessed by end of Spring Semester|
Summary of Content: The increase in speed and memory capacity of modern computers has dramatically changed their use and applicability for complex statistical analysis. This module explores how computers allow the easy implementation of standard, but computationally intensive, statistical methods and also explores their use in the solution of non-standard analytically intractable problems by innovative numerical methods. The material builds on the theory of the module G13INF to cover several topics that form the basis of some current research areas in computational statistics. Particular topics to be covered include a selection from simulation methods, Markov chain Monte Carlo methods, the bootstrap and nonparametric statistics, statistical image analysis, and wavelets. Students will gain experience of using a statistical package and interpreting its output.
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||21 weeks||1 per week||2 hours|
|Computing||10 weeks||1 per week||1 hour|
Method of Assessment:
|Exam 1||80||2 hour 30 minute written examination|
|Coursework 1||10||Exercise 1|
|Coursework 2||10||Exercise 2|
Dr C Fallaize
Education Aims: The purpose of this module is to deepen and broaden the students' knowledge and experience of statistics by studying the key concepts and theory of some advanced topics in computational statistics that form the basis of current statistical research.
This module is in the Statistics Pathway and builds upon the statistical ideas and methods introduced in the module G13INF. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.
Learning Outcomes: A student who completes this module successfully will be able to:
L1 - state and prove standard results relating to the theory and methods of the topics in computational statistics;
L2 - derive, calculate and explain properties of the methods;
L3 - derive appropriate point and interval estimators, and construct suitable test procedures for the topic areas;
L4 - apply the theory and methods to a range of appropriate examples;
L5 - implement selected computational methods using a statistical software package;
L6 - explain and interpret statistical results in the context of computational statistics.
Offering School: Mathematical Sciences
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