Multiple-try Metropolis

Multiple-try Metropolis (MTM) is a sampling method that is a modified form of the Metropolis-Hastings method, first presented by Liu, Liang, and Wong in 2000. ^[1]

7 relations: Chi distribution, Detailed balance, Markov chain Monte Carlo, Metropolis–Hastings algorithm, Multivariate normal distribution, Probability distribution, Sampling (statistics).

Chi distribution

No description.

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The principle of detailed balance is formulated for kinetic systems which are decomposed into elementary processes (collisions, or steps, or elementary reactions): At equilibrium, each elementary process should be equilibrated by its reverse process.

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Markov chain Monte Carlo

In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution.

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Metropolis–Hastings algorithm

In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult.

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Multivariate normal distribution

In probability theory and statistics, the multivariate normal distribution or multivariate Gaussian distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions.

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Probability distribution

In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.

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Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population.

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Multiple-Try Metropolis.

References

[1] https://en.wikipedia.org/wiki/Multiple-try_Metropolis

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