Treffer: The multiple-try method and local optimization in metropolis sampling

Title:
The multiple-try method and local optimization in metropolis sampling
Authors:
LIU, J. S, LIANG, F, WONG, W. H
Source:
Journal of the American Statistical Association. 95(449):121-134
Publisher Information:
Alexandria, VA: American Statistical Association, 2000.
Publication Year:
2000
Physical Description:
print, 22 ref
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Processus de markov, Markov processes, Statistiques, Statistics, Théorie de l'échantillonnage, sondages statistiques, Sampling theory, sample surveys, Lois de probabilités, Distribution theory, Inférence linéaire, régression, Linear inference, regression, Chaîne Markov, Markov chain, Cadena Markov, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Echantillonnage, Sampling, Muestreo, Estimation adaptative, Adaptive estimation, Estimación adaptativa, Mélange, Mixture, Mezcla, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode gradient conjugué, Conjugate gradient method, Método gradiente conjugado, Sinusoïde, Sinusoid, Sinusoide, ADS algorithm, Adaptive directional sampling algorithm, Echantillonnage Gridy Gibbs, Gridy Gibbs sampler, Echantillonnage Metropolis Hastings, Metropolis Hastings sampler, MCMC simulation, Markov chain Monte Carlo simulation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, Stanford University, Stanford, CA 94305, United States
Department of Statistics and Applied Probability, the National University of Singapore, Singapore 119260, Singapore
Department of Statistics, University of California, Los Angeles, CA 90095, United States
ISSN:
0162-1459
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=1485268
Rights:
Copyright 2000 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.1485268
Database:
PASCAL Archive

Weitere Informationen

This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring in using the rule, the multiple-try method can also be exploited to achieve the effect of a griddy Gibbs sampler without having to bear with griddy approximations, and the effect of a hit-and-run algorithm without having to figure out the required conditional distribution in a random direction.