ACM 206

Topics in Computational Mathematics

9 units (3-0-6)    |  third term
Prerequisites: ACM 106 ab; linear algebra at the level of ACM 104 or ACM 107; probability theory at the level of ACM 116 or ACM 117; some programming experience.

This course provides an introduction to Monte Carlo methods with applications in Bayesian computing and rare event sampling. Topics include Markov chain Monte Carlo (MCMC), Gibbs samplers, Langevin samplers, MCMC for infinite-dimensional problems, convergence of MCMC, parallel tempering, umbrella sampling, forward flux sampling, and sequential Monte Carlo. Emphasis is placed both on rigorous mathematical development and on practical coding experience.

Instructor: Tropp

Please Note

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