Information about Test

  1. Markov chain Monte Carlo

    Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that

  2. Monte Carlo method

    mathematicians often use a Markov chain Monte Carlo (MCMC) sampler. The central idea is to design a judicious Markov chain model with a prescribed stationary

  3. Markov model

    distribution of a previous state. An example use of a Markov chain is Markov chain Monte Carlo, which uses the Markov property to prove that a particular method

  4. Markov chain

    population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used for simulating

  5. Hamiltonian Monte Carlo

    and statistics, the Hamiltonian Monte Carlo algorithm (also known as hybrid Monte Carlo), is a Markov chain Monte Carlo method for obtaining a sequence

  6. Reversible-jump Markov chain Monte Carlo

    computational statistics, reversible-jump Markov chain Monte Carlo is an extension to standard Markov chain Monte Carlo (MCMC) methodology that allows simulation

  7. Metropolis–Hastings algorithm

    and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a

  8. Markov chain mixing time

    Markov chain is the time until the Markov chain is "close" to its steady state distribution. More precisely, a fundamental result about Markov chains

  9. Hidden Markov model

    prediction, more sophisticated Bayesian inference methods, like Markov chain Monte Carlo (MCMC) sampling are proven to be favorable over finding a single

  10. Markov chain central limit theorem

    Geyer, Charles J. (2011). Introduction to Markov Chain Monte Carlo. In Handbook of MarkovChain Monte Carlo. Edited by S. P. Brooks, A. E. Gelman, G. L