STATS 202C

Monte Carlo Methods for Optimization

Description: Lecture, three hours; discussion, one hour. Requisite: course 202B. Monte Carlo methods and numerical integration. Importance and rejection sampling. Sequential importance sampling. Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on Gibbs samplers and Metropolis/Hastings. Simulated annealing. Exact sampling with coupling from past. Permutation testing and bootstrap confidence intervals. S/U or letter grading.

Units: 4.0
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Mark Handcock

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Karen McKinnon

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Tianyi Sun

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Song-Chun Zhu

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Yaxuan Zhu

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