Population Markov Chain Monte Carlo

Population Markov Chain Monte Carlo (2003)

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by Kathryn Blackmond Laskey , James Myers

Venue:	Machine Learning
Citations:	7 - 1 self

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Datum	Value	Source
TITLE	Population Markov Chain Monte Carlo	INFERENCE
AUTHOR NAME	Kathryn Blackmond Laskey	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of Systems Engineering and; Operations Research, MS 4A5; George Mason University	SVM HeaderParse 0.2
AUTHOR ADDR	Fairfax, VA 22030	SVM HeaderParse 0.1
AUTHOR NAME	James Myers	SVM HeaderParse 0.1
AUTHOR AFFIL	Research and Evaluation Division; Ballistic Missile Defense Organization	SVM HeaderParse 0.2
AUTHOR ADDR	Pentagon, DC 220330	SVM HeaderParse 0.1
ABSTRACT	Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions. KEY WORDS: Markov Chain Monte Carlo, Metropolis-Hastings Algorithm, Graphical Probabilistic Models, Bayesian Networks, Bayesian Learning, Evolutionary Algorithms Machine Learning MCMC Issue 1 5/16/01 1.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	175--196	INFERENCE
VOLUME	50	INFERENCE
CITATIONS	50 found	ParsCit 1.0