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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Enrollment Progress

Jul 9, 4 PM PDT
LEC 3: 33/80 seats taken (Open)
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Section List

  • LEC 3

    Closed

    TR 2pm-3:15pm

    Dodd Hall 175

Course

Previously taught
22F 21F 19F 16S 13S 12S 11S 10S 09S

Previous Grades

A+AA-B+BB-C+CC-D+DD-F0%5%10%15%