Introducing Monte Carlo Methods with R: a first course

George Casella will teach a two-day course based on the book on March 8 and 9, on the campus of the University of Florida.

This course is a self-contained entry into Monte Carlo computational techniques. The emphasis on practice is a major feature of this course, which is aimed at graduate students and faculty who need to use simulation methods as a tool to analyze their experiments and/or datasets. The course should appeal to scientists in all fields, given the versatility of these Monte Carlo tools. The choice of the programming language R as opposed to faster alternatives like Matlab or C, and to more structured constructs like WinBUGS is due to its simplicity and to its versatility.

The course starts with an introduction to R, and then uses R to generate random variables, both standard and nonstandard. We then cover the important topics of Monte Carlo Integration and Optimization, before starting on MCMC (Markov chain Monte Carlo) techniques such as the Metropolis-Hastings Algorithm and the Gibbs sampler. Throughout the course the techniques are illustrated with many examples and data sets. The course is based on the recent book Introducing Monte Carlo Methods with R, by Christian Robert and George Casella (2009, Springer-Verlag).

I will also teach a very short course on March 17 in San Antonio,  Texas, based on the book, in the first day of the meeting Frontiers of Statistical Decision Making and Bayesian Analysis, in honour of Jim Berger,

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