Monte Carlo Markov Chains

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Markov Chain Monte Carlo: Sampling and Convergence

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Optimization and Simulation

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

Markov Chains and Algorithm Applications

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

MCMC with Metropolis

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

Modeling Neurobiological Signals: Markov Chains

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

Quasi-Stationary Distribution: Molecular Dynamics Modeling

Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.

Markov Chains and Algorithm Applications

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Monte Carlo: Optimization and Estimation

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

Monte Carlo: Markov Chains

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

Quantum Monte Carlo: Basic Concepts

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