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. 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

  3. 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

  4. 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

  5. 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

  6. 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

  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. Construction of an irreducible Markov chain in the Ising model

    irreducible Markov Chain in the Ising model is the first step in overcoming a computational obstruction encountered when a Markov chain Monte Carlo method

  9. 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

  10. Parallel tempering

    improving the dynamic properties of Monte Carlo method simulations of physical systems, and of Markov chain Monte Carlo (MCMC) sampling methods more generally