Catalogue of Modules, University of Nottingham

G14CST Computational Statistics
(Last Updated:03 May 2017)

Year  17/18

Total Credits: 20

Level: Level 4

Target Students:  Single Honours students. MSc students.  Available to JYA/Erasmus students.

Taught Semesters:

Full Year Assessed by end of Spring Semester 


G13INF Statistical Inference 

Corequisites:  None.

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:

ActivityNumber Of WeeksNumber of sessionsDuration of a session
Lecture 21 weeks1 per week2 hours
Computing 10 weeks1 per week1 hour

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Further Activity Details:
Two hours of lectures per week, although some slots will not be used (eg at times when students are working on assessed coursework). Additional computing labs will be arranged as appropriate. Total nominal contact time is 36 hours.

Method of Assessment: 

Assessment TypeWeightRequirements
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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