STATS 102C · STAT 102CIntroduction to Monte Carlo Methods
Statistics · 4 units · Undergraduate upper division (100-199)
Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection sampling and importance sampling and their roles in MCMC. Markov chain theory and convergence properties. Metropolis and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems.
P/NP or letter grading.
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Requisites
Official UCLA wording
Requisites: courses 100B (or Mathematics 170S), 102A.
BruinTree reads · Prerequisite
needs reviewconfidence 0.90 · from text- · "MATH 102A" is not in this catalog version
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS 102C
- STATS 100BIntroduction to Mathematical Statistics
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
- MATH 170EIntroduction to Probability and Statistics 1: Probability
- MATH 170SIntroduction to Probability and Statistics 2: Statistics
2 direct requisites. Showing 18 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
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