Stats 102C Introduction to Monte Carlo Methods
Course description: Introduction to Monte Carlo algorithms for scientific computing. Generation of random numbers from specific distributions. Rejection method, importance sampling and sequential Monte Carlo. Introduction to Markov chain theory and convergence properties. The Metropolis-Hastings and the Gibbs sampling algorithms. Extensions to simulated annealing, parallel tempering and the equi-energy sampler. Theoretical understanding of methods and their implementation in concrete computational problems in Bayesian statistics, computational biology and statistical physics.
Instructor: Qing Zhou, Department of Statistics, zhou@stat.ucla.edu
Lectures: MWF 3-3:50pm, BOELTER 5264.
Office Hours:
W 4-6 pm, MS 8979.
TA:
Nicole Chen,
nicolechen@stat.ucla.edu
Discussion section: Thu 3-3:50pm, MS 5127.
Office Hour: Wed 11-12, MS 8105G.
Reference books:
1)
Liu JS, Monte Carlo strategies in scientific computing,
Springer, 2nd printing edition (January 4, 2008). Optional.
2) Karlin S and Taylor HM, An introduction to
stochastic modeling, Academic Press, 3rd edition (1998). Optional.