Results 281 to 290 of about 112,632 (333)
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2010
The Markov chain Monte Carlo (MCMC) revolution sweeping statistics is drastically changing how statisticians perform integration and summation. In particular, the Metropolis algorithm and Gibbs sampling make it straightforward to construct a Markov chain that samples from a complicated conditional distribution.
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The Markov chain Monte Carlo (MCMC) revolution sweeping statistics is drastically changing how statisticians perform integration and summation. In particular, the Metropolis algorithm and Gibbs sampling make it straightforward to construct a Markov chain that samples from a complicated conditional distribution.
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Monte Carlo / Monte Carlo Markov Chain
2014The Monte Carlo simulation is a versatile method for analyzing the behavior of some activities, plans or processes that involve uncertainty. The method was invented by scientists working on the atomic bomb in the 1940s. It uses randomness to obtain random variable estimates, similarly to the gambling process.
Castellano R., CEDROLA, ELENA
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In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling
Nature Electronics, 2021Thomas Dalgaty +5 more
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2016
Monte Carlo Markov Chains (MCMC) are a powerful method to analyze scientific data that has become popular with the availability of modern-day computing resources. The basic idea behind an MCMC is to determine the probability distribution function of quantities of interest, such as model parameters, by repeatedly querying datasets used for their ...
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Monte Carlo Markov Chains (MCMC) are a powerful method to analyze scientific data that has become popular with the availability of modern-day computing resources. The basic idea behind an MCMC is to determine the probability distribution function of quantities of interest, such as model parameters, by repeatedly querying datasets used for their ...
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Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
, 1995P. Green
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2020
can be used if one is able to compute the integral analytically, which is seldom the case.
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can be used if one is able to compute the integral analytically, which is seldom the case.
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Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference
Technometrics, 2008S. Ahmed
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