Markov Chains and Applications

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Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Covers Markov chains, Monte Carlo methods, low discrepancy sequences, and multidimensional integrals computation.

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

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.

Covers the theory of Markov chains and Monte Carlo methods.

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