MATH38091 - 2011/2012
- Title: Statistical Computing
- Unit code: MATH38091
- Credits: 10
- This course unit cannot be taken as well as MATH48091 which is a level 4 version of the same course unit.
- Prerequisites: MATH20701, MATH20802 or MATH20812
- Co-requisite units:
- School responsible: Mathematics
- Members of staff responsible: Dr Peter Neal
Specification
Aims
To introduce the student to computational statistics, both the underlying theory and the practical applications.
Brief Description of the unit
Computers are an invaluable tool to modern statisticians. The increasing power of computers has greatly increased the scope of inferential methods and the type of models which can be analysed. This has led to the development of a number of computationally intensive statistical methods, many of which will be introduced in this course.
Learning Outcomes
On successful completion of this course unit students will be able to
- appreciate the usefulness of computational methods in modern statistics;
- understand the basic ideas underpinning the theory;
- be able to apply the methodology to standard problems.
Future topics requiring this course unit
MATH48122 Computationally Intensive Statistic
Syllabus
- Simulating random variables: inversion, rejection, ratio of uniforms, transformations. [4]
- Monte Carlo integration: introduction, importance sampling, antithetic variables, control variates. [4]
- Kernel density estimation. [2]
- Non-parametric Bootstrap and Jackknife. [3]
- Nonlinear regression: model specification, least squares estimation, Gauss-Newton algorithm. [3]
Textbooks
Rizzo, M. L., Statistical Computing with R. Chapman & Hall.
Teaching and learning methods
Weeks 1-5: Two lectures + two hour computer workshop each week. Weeks 7-12: One lecture + one hour computer workshop each week. Students are expected to spend at least 6 hours of study per week (including classes) for this course.
Assessment
- Six pieces of coursework (Weeks 3,5,7,8,10,11): 50%
- End of semester written examination (1.5 hours): 50%