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Abstract:
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This course describes the use of the statistical system R in Monte Carlo experiments, simulation-based inference, and Bayesian computation. R tools are described for generating random variables, computing criteria of statistical procedures, and replicating the procedure to compute quantities such as mean squared error and probability of coverage. R commands for implementing simulation-based procedures such as bootstrap and permutation tests are outlined. The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms. It is assumed the participant will be familiar with the basics of the R system.
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