Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 218,463,629 papers from all fields of science
Search
Sign In
Create Free Account
Markov chain Monte Carlo
Known as:
Monte Carlo markov chain
, Markov Chain Monte Carlo Simulations
, Markov clustering
Expand
In statistics, Markov chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a probability distribution based on constructing a…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
50 relations
Ancestral reconstruction
Bayesian inference in phylogeny
BlackBox Component Builder
Cluster analysis
Expand
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM(ABC)
M. Sadegh
,
J. Vrugt
2014
Corpus ID: 33965143
The quest for a more powerful method for model evaluation has inspired Vrugt and Sadegh (2013) to introduce “likelihood‐free…
Expand
Highly Cited
2012
Highly Cited
2012
Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
C. Andrieu
,
M. Vihola
2012
Corpus ID: 88512180
We study convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms (Andrieu and Roberts [Ann. Statist. 37…
Expand
Highly Cited
2011
Highly Cited
2011
Introduction to Markov Chain Monte Carlo
C. Geyer
2011
Corpus ID: 124524531
Despite a few notable uses of simulation of random processes in the pre-computer era (Hammersley and Handscomb, 1964, Section 1.2…
Expand
Highly Cited
2010
Highly Cited
2010
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
F. Liang
,
Chuanhai Liu
,
R. Carroll
2010
Corpus ID: 118728102
Preface. Acknowledgments. Publisher's Acknowledgments. 1 Bayesian Inference and Markov Chain Monte Carlo. 1.1 Bayes. 1.1.1…
Expand
Review
2010
Review
2010
A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis
M. C. Edwards
2010
Corpus ID: 120567920
Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal…
Expand
Highly Cited
2010
Highly Cited
2010
Likelihood-free Markov chain Monte Carlo
S. Sisson
,
Y. Fan
2010
Corpus ID: 88517733
To appear to MCMC handbook, S. P. Brooks, A. Gelman, G. Jones and X.-L. Meng (eds), Chapman & Hall.
Highly Cited
2005
Highly Cited
2005
An efficient two‐stage Markov chain Monte Carlo method for dynamic data integration
Y. Efendiev
,
A. Datta-Gupta
,
Victor Ginting
,
Xianlin Ma
,
B. Mallick
2005
Corpus ID: 18307646
In this paper, we use a two‐stage Markov chain Monte Carlo (MCMC) method for subsurface characterization that employs coarse…
Expand
Review
2004
Review
2004
Improving Markov Chain Monte Carlo Model Search for Data Mining
Paolo Giudici
,
R. Castelo
Machine-mediated learning
2004
Corpus ID: 18041873
The motivation of this paper is the application of MCMC model scoring procedures to data mining problems, involving a large…
Expand
Highly Cited
2004
Highly Cited
2004
Population Markov Chain Monte Carlo
Kathryn B. Laskey
,
James W. Myers
Machine-mediated learning
2004
Corpus ID: 5087957
Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed…
Expand
Highly Cited
1998
Highly Cited
1998
Adaptive Markov Chain Monte Carlo through Regeneration
W. Gilks
,
G. Roberts
,
S. Sahu
1998
Corpus ID: 14692124
Abstract Markov chain Monte Carlo (MCMC) is used for evaluating expectations of functions of interest under a target distribution…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE