Description: Lecture, three hours. Recommended requisite: course 200B. Recommended preparation: programming skills in R, C/C++, MATLAB. Overview of theory and practice of computer-based methods for statistical inference and uncertainty quantification, including bootstrap, resampling, computer simulation, and Monte Carlo sampling. Coverage of nonparametric and parametric bootstrap, bootstrap inference, permutation test, cross-validation, likelihood approximation, importance sampling, and Markov chain Monte Carlo with brief introduction to Bayesian inference and missing data problems. S/U or letter grading.

Units: 4.0