From Metropolis to diffusions: Gibbs states and optimal scaling.

DOI10.1016/S0304-4149(00)00041-7zbMath1047.60065OpenAlexW1977224304WikidataQ126550807 ScholiaQ126550807MaRDI QIDQ1879490

Gareth O. Roberts, Laird Breyer

Publication date: 22 September 2004

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4149(00)00041-7

zbMATH Keywords

Markov chain Monte CarloGibbs measures

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05)

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Cites Work

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• Gibbs measures and phase transitions
• Mixing: Properties and examples
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Ergodic theorems for subadditive spatial processes
• Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields
• Ergodicity for Infinite Dimensional Systems