University of California, Los Angeles 4 credits Statistics
Lecture, three hours. Recommended requisite: course 200B. Recommended preparation: programming skills in R, C/C++, MATLAB. Overview of theory and practice of computerbased 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.
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