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Unit information: Theory of Inference in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Theory of Inference
Unit code MATH35600
Credit points 20
Level of study H/6
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 (or MATH10013 Probability and Statistics).

Co-requisites

None

School/department School of Mathematics
Faculty Faculty of Science

Description

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.

Transferable Skills: This unit exemplifies the general skills of other mathematical units, of logical thinking and the concept of proof, problem solving, abstraction, a facility with notation, self-study and self-appraisal. Some examples and homeworks will use the statistical computing environment R.

Teaching details

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

Assessment Details

80% written examination

20% coursework

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

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

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