Stochastic Simulation: Markov Chains and Transition Matrices

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Monte Carlo: Markov Chains

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

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Probability & Stochastic Processes

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

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Curie Weiss Model

Covers the Curie-Weiss model in Statistical Physics, including magnetization probability, free entropy, and the cavity method.

Modeling Neurobiological Signals: Markov Chains

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

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Markov Chains: Simulation and Optimization

Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.