Unit information: Theory of Inference 4 in 2019/20

Unit name	Theory of Inference 4
Unit code	MATHM0019
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Wood
Open unit status	Not open
Pre-requisites	MATH11300 Probability 1 and MATH11400 Statistics 1
Co-requisites	None
School/department	School of Mathematics
Faculty	Faculty of Science

Description including Unit Aims

Unit Aims

Statistical inference is about drawing quantitative conclusions about things that we are interested in from data that we can collect. This unit provides an overview of the theory and methods used to do this, comparing the Bayesian and the frequentist approaches, with a practical focus on using the theory in practice.

Unit Description

The course covers statistical model, statistical methods of uncertainty quantification, statistical model comparison, model checking, the difference between inference about causality and association, and the practical implementation of Bayesian and maximum likelihood based approaches.

Relation to Other Units

This units builds on Statistics 1, and uses technical material covered in first year mathematics courses. It complements the more specialized courses in Generalized linear modelling, Bayesian statistics and Statistics 2 (none of which are required as prerequisites).

Intended Learning Outcomes

To gain an understanding of some key principles of statistical inference, and how these impact upon current practice across a range of fields.

Teaching Information

Lectures, problems classes, homeworks to be done by students, Office Hours.

Assessment Information

20% Coursework & 80% Examination

Reading and References

Recommended

• S.N. Wood, Core Statistics, Cambridge University Press, 2015 (also provided free online)
• D.R. Cox, Principles of Statistical Inference, Cambridge University Press, 2006
• A.C. Davison, Statistical Models, Cambridge University Press, 2003
• Daniel Kahneman, Thinking Fast and Slow, Penguin, 2012