Information about Test

  1. Markov chain Monte Carlo

    aimlexchange.com/search/wiki/page/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

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    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. Metropolis–Hastings algorithm

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

  4. PyMC3

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    modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. It is a rewrite from scratch

  5. List of statistical software

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    another Gibbs sampler (JAGS) – a program for analyzing Bayesian hierarchical models using Markov chain Monte Carlo developed by Martyn Plummer. It is

  6. OpenBUGS

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    for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. OpenBUGS is the open source variant of WinBUGS

  7. Stan (software)

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    StataStan - integration with Stata Stan implements gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference, stochastic, gradient-based

  8. MCSim

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    statistical or simulation models, perform Monte Carlo simulations, and Bayesian inference through Markov chain Monte Carlo simulations. The latest version allow

  9. List of uncertainty propagation software

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    carlo method. MUQ is an MIT developed collection of UQ tools for Markov Chain Monte Carlo sampling, Polynomial Chaos construction, transport maps, and many

  10. Bayesian probability

    aimlexchange.com/search/wiki/page/Bayesian_probability

    applications of Bayesian methods, mostly attributed to the discovery of Markov chain Monte Carlo methods and the consequent removal of many of the computational

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