Stochastic Simulation: Markov Chains and Metropolis Hastings

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Markov Chains: Simulation and Optimization

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

Markov Chains and Algorithm Applications

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

Modeling Neurobiological Signals: Markov Chains

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

Markov Chain Games

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

Applied Probability & Stochastic Processes

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

Stochastic Simulation: Markov Chains and Transition Matrices

Explores Markov chains, transition matrices, and Bayesian statistics in stochastic simulation.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Markov Chains: Basics and Applications

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.