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Titolo:
Structured Markov chain Monte Carlo
Autore:
Sargent, DJ; Hodges, JS; Carlin, BP;
Indirizzi:
Mayo Clin, Biostat Sect, Rochester, MN 55905 USA Mayo Clin Rochester MN USA 55905 n, Biostat Sect, Rochester, MN 55905 USA Univ Minnesota, Sch Publ Hlth, Div Biostat, Minneapolis, MN 55455 USA UnivMinnesota Minneapolis MN USA 55455 iostat, Minneapolis, MN 55455 USA
Titolo Testata:
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
fascicolo: 2, volume: 9, anno: 2000,
pagine: 217 - 234
SICI:
1061-8600(200006)9:2<217:SMCMC>2.0.ZU;2-Y
Fonte:
ISI
Lingua:
ENG
Soggetto:
GIBBS SAMPLER; BAYESIAN COMPUTATION; HIERARCHICAL-MODELS; SPATIAL STATISTICS; DIAGNOSTICS; DISTRIBUTIONS; CONVERGENCE; ESTIMATORS; INFERENCE;
Keywords:
blocking; convergence acceleration; Gibbs sampling; hierarchical model; Metropolis-Hastings algorithm;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
45
Recensione:
Indirizzi per estratti:
Indirizzo: Sargent, DJ Mayo Clin, Biostat Sect, 200 1st St SW, Rochester, MN 55905 USA Mayo Clin 200 1st St SW Rochester MN USA 55905 r, MN 55905 USA
Citazione:
D.J. Sargent et al., "Structured Markov chain Monte Carlo", J COMPU G S, 9(2), 2000, pp. 217-234

Abstract

This article introduces a general method for Bayesian computing in richly parameterized models, structured Markov chain Monte Carlo (SMCMC), that is based on a blocked hybrid of the Gibbs sampling and Metropolis-Hastings algorithms. SMCMC speeds algorithm convergence by using the structure that is present in the problem to suggest an appropriate Metropolis-Hastings candidate distribution. Although the approach is easiest to describe for hierarchical normal linear models, we show that its extension to both nonnormal andnonlinear cases is straightforward. After describing the method in detail we compare its performance tin terms of run time and autocorrelation in thesamples) to other existing methods, including the single-site updating Gibbs sampler available in the popular BUGS software package. Our results suggest significant improvements in convergence for many problems using SMCMC, as well as broad applicability of the method, including previously intractable hierarchical nonlinear model settings.

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Documento generato il 25/11/20 alle ore 10:04:13