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What is Markov Chain Monte Carlo (MCMC)

Chapter 608
Encyclopedia of Information Science and Technology, Third Edition
MCMC is a broad term for a class of numerical algorithms used to estimate a posterior distribution, often the posterior distribution of model parameters. Even more generally, MCMC algorithms are used for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution .
Published in Chapter:
Conditional Random Fields for Modeling Structured Data
Tom Burr (Statistical Sciences, Los Alamos National Laboratory, USA) and Alexei Skurikhin (Space Data Systems, Los Alamos National Laboratory, USA)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch608
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Chapter 217
Stochastic Approximation Monte Carlo for MLP Learning
A class of algorithms for sampling from probability distributions by simulating a Markov chain that has the desired distribution as its stationary distribution. The state of the Markov chain after a large number of steps is then used as a sample from the desired distribution.
Published in Chapter: Stochastic Approximation Monte Carlo for MLP Learning; From: Encyclopedia of Artificial Intelligence
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Chapter 23
Bayesian Variable Selection
The class of random sampling methods from probability distributions based on Markov chain. A Gibbs sampler is one of the well known MCMC methods.
Published in Chapter: Bayesian Variable Selection; From: Encyclopedia of Business Analytics and Optimization
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Chapter 5
Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model
is a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution.
Published in Chapter: Inferring Genetic Regulatory Interactions with Bayesian Logic-Based Model; From: Handbook of Research on Computational Methodologies in Gene Regulatory Networks
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