An automated stopping rule for MCMC convergence assessment

DOI10.1007/s001800050024zbMath0947.60018OpenAlexW2060379981MaRDI QIDQ1979100

Jean Diebolt, Didier Chauveau

Publication date: 24 May 2000

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/inria-00073116/file/RR-3566.pdf

zbMATH Keywords

Markov chain Monte Carlo algorithmnormality testfinite state Markov chainconvergence assessment

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Monte Carlo methods (65C05) Continuous-time Markov processes on discrete state spaces (60J27)

Related Items (6)

Recursive estimation of time-average variance constants ⋮ New recursive estimators of the time-average variance constant ⋮ On single versus multiple imputation for a class of stochastic algorithms estimating maximum likelihood ⋮ Central limit theorem for hitting times of functionals of Markov jump processes ⋮ Improving Convergence of the Hastings–Metropolis Algorithm with an Adaptive Proposal ⋮ Estimation of the Asymptotic Variance in the CLT for Markov Chains

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