Aims and Objectives
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
- 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)
- Use report writing and computing skills
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
Teaching and learning methods
|Total study time||100|
Resources & Reading list
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.
Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge: Cambridge University Press.
Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
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). Wiley.
Robert, C. P. and Casella, G. (1999). Monte Carlo Statistical Methods. New York: Springer.
Venables, W. N. and Smith, D. M. (2002). An Introduction to R.
Ripley, B. D. (1987). Stochastic Simulation. New York: Wiley.
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.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External