Result: Regenerative Markov Chain Monte Carlo for Any Distribution
Title:
Regenerative Markov Chain Monte Carlo for Any Distribution
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1745-1760
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 p.1/4
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, Lois de probabilités, Distribution theory, Processus de markov, Markov processes, Statistiques, Statistics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Analyse numérique, Numerical analysis, Análisis numérico, Chaîne Markov, Markov chain, Cadena Markov, Distribution statistique, Statistical distribution, Distribución estadística, Estimation biaisée, Biased estimation, Estimación sesgada, Fonction répartition, Distribution function, Función distribución, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Simulation numérique, Numerical simulation, Simulación numérica, Théorie approximation, Approximation theory, 60E05, 60J10, 62E17, 65C05, 65C40, Markov chain Monte Carlo, Primary 65C0, Regenerative, Secondary 78M31, 80M31, Simulation
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of ISDS, California State University, Fullerton, California, United States
Biosciences Division, Argonne National Laboratory, Argonne, Illinois, United States
Department of Mathematics, California State University, Fullerton, California, United States
Biosciences Division, Argonne National Laboratory, Argonne, Illinois, United States
Department of Mathematics, California State University, Fullerton, California, United States
ISSN:
0361-0918
Rights:
Copyright 2015 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
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.26164020
Database:
PASCAL Archive
Further Information
While Markov chain Monte Carlo (MCMC) methods are frequently used for difficult calculations in a wide range of scientific disciplines, they suffer from a serious limitation: their samples are not independent and identically distributed. Consequently, estimates of expectations are biased if the initial value of the chain is not drawn from the target distribution. Regenerative simulation provides an elegant solution to this problem. In this article, we propose a simple regenerative MCMC algorithm to generate varieties for any distribution.