Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields

Arnoldo Frigessi; Fabio Martinelli; Julian Stander

doi:10.1093/biomet/84.1.1

Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields

Arnoldo Frigessi^*, Fabio Martinelli, Julian Stander

^*Corresponding author for this work

School of Engineering, Computing and Mathematics

Roma Tre University

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	1-18
Number of pages	0
Journal	Biometrika
Volume	84
Issue number	1
DOIs	https://doi.org/10.1093/biomet/84.1.1
Publication status	Published - 1997

Keywords

Gibbs sampler
metropolis algorithm
NP-completeness
range of interaction
rate of convergence
spectral gap
stopping rule

Access to Document

10.1093/biomet/84.1.1

Cite this

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title = "Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields",

keywords = "Gibbs sampler, metropolis algorithm, NP-completeness, range of interaction, rate of convergence, spectral gap, stopping rule",

author = "Arnoldo Frigessi and Fabio Martinelli and Julian Stander",

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doi = "10.1093/biomet/84.1.1",

language = "English",

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journal = "Biometrika",

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AU - Frigessi, Arnoldo

AU - Martinelli, Fabio

AU - Stander, Julian

PY - 1997

Y1 - 1997

KW - Gibbs sampler

KW - metropolis algorithm

KW - NP-completeness

KW - range of interaction

KW - rate of convergence

KW - spectral gap

KW - stopping rule

U2 - 10.1093/biomet/84.1.1

DO - 10.1093/biomet/84.1.1

M3 - Article

SN - 0006-3444

VL - 84

SP - 1

EP - 18

JO - Biometrika

JF - Biometrika

IS - 1

ER -