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. Theoretical understanding of methods and their implementation in concrete computational problems in Bayesian statistics, computational biology and statistical physics.
Link to the course CCLE site for lecture notes, homework assignments, etc.
References:
1) Robert C.P. and Casella, G (2010) Introducing Monte Carlo Methods with R. Springer. Optional.
2) Karlin S and Taylor HM, An introduction to
stochastic modeling, Academic Press, 3rd edition (1998). Optional.