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Topics in Computational Mathematics

California Institute of TechnologyApplied & Computational Math
Credits9
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Semester offeredSummer
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Last updated3 months ago

Description

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. Not offered 2025-26. 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. Instructor: Staff

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ACM206 has possible credit equivalents including MATH10093 at The University of Edinburgh.

CourseUniversityQwest Score
MATH10093
Statistical Computing
The University of Edinburgh64
MATH10093
Statistical Computing
The University of Edinburgh64