Monte Carlo method

Related lectures (32)

Page 3 of 4

Covers the basic concepts of Quantum Monte Carlo and its application in spin chains, Markov chains, and Monte Carlo simulations.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Explores Monte Carlo integration through uncorrelated sampling and its error scaling with the number of points used.

Explores the impact of PVT variations, uncertainties in IC design, worst-case design paradigms, and the importance of Monte-Carlo simulations.

Covers the determination of neighborhood criteria for geographic objects.

Introduces stochastic simulation fundamentals, covering course organization, queueing models, finance, statistics, physics, and exam details.

Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.

Covers specular view factors, radiative exchange, energy transfer, and numerical integration methods in thermal radiation.