Computational Statistics
| Code | School | Level | Credits | Semesters |
| MATH4007 | Mathematical Sciences | 4 | 20 | Full Year UK |
- Code
- MATH4007
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Full Year UK
Summary
The increase in speed and memory capacity of modern computers has dramatically changed their use and applicability for complex statistical analysis. This course 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 course MATH3013 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.
Target Students
Available to single Honours and MSc students.
Classes
- One 2-hour lecture each week for 20 weeks
- One 1-hour computing each week for 10 weeks
Assessment
- 10% Coursework 1: Exercise 1
- 10% Coursework 2: Exercise 2
- 80% Exam 1 (3-hour): Written examination.
Assessed by end of spring semester
Educational Aims
The purpose of thiscourse 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.Thiscourse is in the Statistics Pathway and builds upon the statistical ideas and methods introduced in the module MATH3013. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.Learning Outcomes
A student who completes this course 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.