## Current Teaching

I currently teach the following modules. For people with a valid University of Kent username, supporting material for these modules is available via Moodle.

### MA025: Foundation Statistics

This module for Foundation Year students gives a basic introduction to probability and statistics. Topics covered include graphical presentation of data, introductory probability, including axioms, independence and conditional probability, the law of total probability and Bayes theorem, along with an introduction to common discrete and continuous distributions. In the statistical part we cover point and interval estimation.

### MA319: Probability and Statistics for Actuarial Science

I teach the statistics part of this undergraduate module. It is a standard introduction to statistical inference, mostly based on the normal distribution and related distributions such as the t, chi-squared and F distributions. We cover sampling distributions, point estimation, interval estimation, hypothesis tests, association between variables, goodness of fit and a brief introduction to maximum likelihood estimation.

### MA881: Probability and Classical Inference

This is a fairly standard masters level module on probability and mathematical statistics from a frequentist viewpoint. The Bayesian approach to inference is covered in a separate module. In focus of the probability part is on the aspects of probability theory that we need for the inference part, including an introduction to different types of convergence. The inference part is mainly focused on likelihood-based methods.

## Previous Teaching

Modules that I have taught in the recent past include:

### MA890: Practical Statistics and Computing

This module is taken by students on the MSc Statistics and MSc Statistics with Finance. The module is focused on statistical computing in R and aims to cover the use of R both to carry out standard statistical analyses and as a programming language. The module also covers some basic statistical techniques, including for example non-parametric tests and goodness of fit tests. The module is assessed entirely by coursework, involving programming exercises and practical data analysis.

### MA888: Stochastic Models in Ecology and Medicine

This is an optional module for MSc Statistics students. I teach the Ecology part of the module, which deals with the analysis of data collected on wild animals. Particular attention will be given to estimating how long wild animals live, and also to estimating the sizes of mobile animal populations. Fitted probability models will allow the assessment of environmental changes, such as global warming. Complex models may contain too many parameters to be estimated, and procedures will be described for testing for this using symbolic computer packages. State-space models provide a unifying framework for many of the models considered. Both classical and Bayesian methods of inference are considered.