The University of Southampton
Courses

STAT6079 Computer Intensive Statistical Methods

Module Overview

Aims and Objectives

Module Aims

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

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Use IT skills developed by tackling computing problems through the use of a specific package (R).
  • Demonstrate knowledge and understanding of the EM algorithm and extensions, and Markov chain Monte Carlo methods
  • Use report writing and computing skills
  • Write or modify S-Plus or R functions to implement these techniques and use them for model fitting or data analysis
  • Demonstrate knowledge and understanding of the basic ideas of random number generation, re-sampling and simulation methods (bootstrap)

Syllabus

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

TypeHours
Independent Study80
Teaching20
Total study time100

Resources & Reading list

Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and their Application. 

An Introduction to R..

Venables, W. N. and Smith, D. M. (2002). An Introduction to R. 

Other. 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.

Venables, W. N. and Ripley, B. D. (1996, 1997, 1999, 2002). Modern Applied Statistics with S(- Plus). 

Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. 

Little, R. and Rubin, D.B. (2001). Statistical Analysis with Missing Data (Chapter 8). 

Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. 

Tanner, M. A. (1991, 1993, 1996). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. 

Robert, C. P. and Casella, G. (1999). Monte Carlo Statistical Methods. 

Ripley, B. D. (1987). Stochastic Simulation. 

Gamerman, D. (1997). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. 

Assessment

Summative

MethodPercentage contribution
Coursework assignment(s) 100%

Referral

MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: STAT6083

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