MCMC Examples and Error Estimation

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

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

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

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.

Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.

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

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

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.

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