Applied Stochastic Modelling and Data Analysis - MA771

Location Term Level Credits (ECTS) Current Convenor 2017-18 2018-19
Canterbury Spring
View Timetable
6 15 (7.5) DR DJ Cole


MA629 Probability and Inference or MA529 Probability and Statistics for Actuarial Science 2; MA632 Regression; (MA584 Computational Mathematics, would be useful but not necessary).





This applied statistics module focusses on problems that occur in the fields of ecology, biology, genetics and psychology. Motivated by real examples, you will learn how to define and fit stochastic models to the data. In more complex situations this will mean using optimisation routines in MATLAB to obtain maximum likelihood estimates for the parameters. You will also learn how construct, fit and evaluate such stochastic models. Outline Syllabus includes: Function optimisation. Basic likelihood tools. Fundamental features of modelling.  Model selection. The EM algorithm. Simulation techniques. Generalised linear models.


This module appears in:

Contact hours

32 hours of lectures and 8 hours of terminal classes

Method of assessment

80% Examination, 20% Coursework

Preliminary reading

B J T Morgan Applied Stochastic Modelling (2009). (2nd ed., CRC Press) (E)
McCullagh, P. and Nelder, J. A. (1989) Generalized linear models, Chapman and Hall.

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes
On successful completion of the module, students will:
a) Have revised and integrated the main probability and statistical material of a standard undergraduate degree programme.
b) Have encountered a range of complex data.
c) Have an appreciation of probability models may be formulated for atypical data sets.
d) Have a good understanding of how likelihood-based classical procedures operate in practice.
e) Have experience of running a wide range of modern statistical procedures through running computer programs in MATLAB.

The intended generic learning outcomes
On successful completion of the module, students will:
a) Appreciate the importance of computing for modern statistical analysis.
b) Appreciate the breadth and importance of modern statistical methods.
c) Be able to describe a number of practical areas where statistical modelling is of importance.
d) Have enhanced their computer skills.
e) Have improved their ability to communicate effectively, and to work independently.
f) Have improved their skills in numeracy, problem solving, computing and written communication.

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