| Description: |
This course is a practical introduction to computationally intensive methods for statistical modelling and inference. Topics covered will be chosen from: the jacknife and bootstrap methods for bias correction and variance estimation; permutation tests; maximum likelihood estimation using the EM algorithm; the Metropolis-Hastings algorithm and Markov Chain Monte Carlo sampling; Bayesian graphical modelling using Gibbs sampling. This course will involve programming in the R language, and use of the WinBUGS package. It is desirable that students enrolling in this course have some programming experience. |
Year2010
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