<p>Lecture, three hours; discussion, one hour. Requisite: course 202B. Monte Carlo methods and numerical integration. Importance and rejection sampling. Sequential importance sampling. Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on Gibbs samplers and Metropolis/Hastings. Simulated annealing. Exact sampling with coupling from past. Permutation testing and bootstrap confidence intervals. S/U or letter grading.</p>

Statistics

Combining rigorous, inclusive pedagogy with opportunities for co-curricular activities—including research, internships, and community engagement—the College provides students with diverse learning experiences that helps them thrive, become critical thinkers, and transform society.

College of Letters and Science

The department introduces students to the science of data through three sequences: theoretical statistics, data analysis, and statistical computing.

General Catalog

Monte Carlo Methods for Optimization

STATS 202C

4 units