Introductory description

This module runs in term 1 and is core for students on an MSc in Statistics course. It is not available for undergraduate students.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module content includes thorough grounding in classical methods of statistical inference with an introduction to more recent developments in statistical methodology. The following items are going to be covered: data, probability, random variables, special univariate distributions, joint and conditional distributions, distributions of functions of random variables, methods of inference, inference using simulation, maximum likelihood estimation, Baysian inference, general linear model.

Learning outcomes

By the end of the module, students should be able to:

• Understand basic probability and random variables.
• Make sense of univariate distributions, joint and conditional distributions and functions of random variables.
• Understand the principles of inference in particular Baysian inference and Maximum Likelihood Estimation.
• Apply linear models in general situations.
• Understand principles of and be able to apply statistical testing using the Likelihood Ratio approach.
• Gain familiarity with basic topics in computational statistics such as importance sampling, rejection sampling etc

Indicative reading list

Casella, G. and Berger, R. L., Statistical Inference, 2nd Ed, Duxbury.
Wasserman L.,All of Statistics: A Concise Course in Statistical Inference, Springer
An Introduction to Probability and Statistical Inference (second edition), by G.G. Roussas
Lecture notes will cover everything that is done in the course.

View reading list on Talis Aspire

Subject specific skills

TBC

Transferable skills

TBC