The University of Southampton
Social Sciences: Social Statistics & DemographyPart of Social SciencesPostgraduate study

STAT6079 Computer Intensive Statistical Methods

Module Overview

To introduce and apply a number of recently developed statistical methods which require a large amount of computer power.

Aims and Objectives

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The basic ideas of random number generation, re-sampling and simulation methods (bootstrap)
  • The EM algorithm and extensions, and Markov chain Monte Carlo methods

Transferable and Generic

Having successfully completed this module, you will be able to:

  • Develop your IT skills by tackling computing problems through the use of a specific package (R).

Subject Specific Practical

Having successfully completed this module, you will be able to:

  • Write or modify S-Plus or R functions to implement these techniques and use them for model fitting or data analysis


The following topics will be covered: basic concepts of programming, an introduction to R, random number generation, re-sampling and simulation methods (bootstrap), the EM algorithm and extensions, and Markov chain Monte Carlo methods. 

Learning and Teaching

Resources and reading list

  • Venables, W. N. and Smith, D. M. (2002). An Introduction to R. ISBN: 0954161742.
  • Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge: CambridgeUniversity Press.
  • Gamerman, D. (1997). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. London: Chapman and Hall.
  • Little, R. and Rubin, D.B. (2001). Statistical Analysis with Missing Data (Chapter 8), 2nd Ed. Wiley.

There will be a blackboard site where all the module materials (slides, computer worksheets, assignments, list of books, etc.) will be made available. You will require access to R.

  • W. N. Venables and D. M. Smith (2005). An Introduction to R.

  • Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
  • Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
  • Ripley, B. D. (1987). Stochastic Simulation. New York: Wiley.
  • Robert, C. P. and Casella, G. (1999). Monte Carlo Statistical Methods. New York: Springer.
  • Tanner, M. A. (1991, 1993, 1996). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. New York: Springer-Verlag.
  • Venables, W. N. and Ripley, B. D. (1996, 1997, 1999, 2002). Modern Applied Statistics with S(-Plus). New York: Springer-Verlag.


Assessment methods

Assessment Method Hours % contribution to final mark Feedback
Coursework One coursework assignment which will involve writing or modifying R functions to implement the techniques introduced during the module.   100%

Referral Method: By set coursework assignment(s)


Costs associated with this course

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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