STATS 202C
Monte Carlo Methods for Optimization
Statistics · 4 units · Graduate courses (200-299)
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.
Requisites
Official UCLA wording
Requisite: course 202B.
BruinTree reads · Prerequisite
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS 202C
- STATS 202BMatrix Algebra and Optimization
- STATS 202AStatistics Programming
1 direct requisite. The whole upstream is here — 2 courses over 2 levels. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.