On the geometrical convergence of Gibbs sampler in \(\mathbb R^d\)

DOI10.1006/jmva.1997.1735zbMath1130.65303OpenAlexW2048830095MaRDI QIDQ1268005

K. Appert

Publication date: 14 October 1998

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1997.1735

zbMATH Keywords

Markov chainGibbs samplerMetropolis algorithmStochastic relaxationgeometrical convergenceMonte Carlo Markov chainHarris recurrencenonlinear autoregression

Mathematics Subject Classification ID

Related Items (3)

Almost sure convergence of the Kaczmarz algorithm with random measurements ⋮ Singularly perturbed Markov chains: Convergence and aggregation ⋮ Accelerating diffusions

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• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
• Metropolis-Type Annealing Algorithms for Global Optimization in $\mathbb{R}^d $

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