Computational Statistics: Monte Carlo and Optimization

Harvard FAS

Computational tools for statistical inference and learning, with emphasis on the computational aspects of statistics, the statistical implications of inference algorithms, and the mathematical foundations for these ideas. Topics include: optimization methods such as Newton-Raphson and gradient-based algorithms; the EM algorithm; variational approximations; Monte Carlo methods, including Markov chain Monte Carlo, importance sampling, data augmentation, and sequential Monte Carlo; generative ML…