| Unit name | Statistical Computing |
|---|---|
| Unit code | MATHM6007 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Didelez |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
This unit provides a practical introduction to the fundamentals of numerical computation for students of statistics. It will cover basic numerical linear algebra, optimization methods, numerical differentiation and integration and techniques for random number generation. The general prerequisites are a working knowledge of the statistical package R (and preferably some knowledge of a lower level language such as C, Pascal or even Fortran), together with a basic familiarity with standard undergraduate results in linear algebra and calculus. By the end of the units, students should be able to write stable, fast and numerically accurate statistical code. The topics covered may include: Numerical linear algebra (with applications): basic efficiency, Choleski, QR, stability, eigen and singular value decompositions, public LA libraries. Optimization: Newton type methods, Gauss-Newton, stochastic optimization. Differentiation by computer: finite differencing (proper interval choice), automatic differentiation. Numerical Integration: quadrature, ODE methods, stochastic. The basics of random number generation
