Biased Monte Carlo Markov Chain

Related lectures (32)

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Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

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

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

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

Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

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

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.

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

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.