Introduction to Monte Carlo Methods

Lecture, three hours; discussion, one hour. Requisites: courses 100B (or Mathematics 170S), 102A. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection sampling and importance sampling and their roles in MCMC. Markov chain theory and convergence properties. Metropolis and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.

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Course

Instructor
Yingnian Wu
Previously taught
22Su 17S 16S 15S 14S

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