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Alternate years
Course Description: 

This course covers statistical methods that would not be possible without the advances made in modern computing over the last 25-30 years.  Specifically, these are simulation and Monte Carlo techniques that are appropriate where statistical theory does not yet provide a solution. Each of the statistical methods covered is computationally intensive and therefore requires a computer to arrive at a solution. Topics include techniques for simulating of random variables, Bayesian statistics, Markov chains, the Metropolis-Hastings algorithm, MCMC and Gibbs Sampling, mixture models, and classification schemes. Prerequisite: Computer Science 131 and either Mathematics 220 or Mathematics 375. One unit.

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