STATS 102C

Introduction to Monte Carlo Methods

Description: Lecture, three hours; discussion, one hour. Requisite: course 100B. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection and importance sampling and its role in MCMC. Markov chain theory and convergence properties. Metropolois and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.

Units: 4.0
1 of 1
1 of 1

Adblock Detected

Bruinwalk is an entirely Daily Bruin-run service brought to you for free. We hate annoying ads just as much as you do, but they help keep our lights on. We promise to keep our ads as relevant for you as possible, so please consider disabling your ad-blocking software while using this site.

Thank you for supporting us!