1. ### Markov chain Monte Carlo

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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. ### 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

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. ### 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

7. ### 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

8. ### 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

9. ### Stochastic

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on) that Monte Carlo methods began to be studied in depth. In the 1950s they were used at Los Alamos for early work relating to the development of the hydrogen

10. ### Bayesian probability

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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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