MCMC Examples and Error Estimation

Related lectures (32)

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

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

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

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

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.

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

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

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