Implementing componentwise Hastings algorithms

DOI10.1016/j.csda.2004.02.002zbMath1429.62024OpenAlexW2003544333MaRDI QIDQ957118

William G. Hanley, John J. Nitao, Zhaoxia Yu, Richard A. Levine

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.02.002

zbMATH Keywords

Markov chain Monte CarloGibbs samplerMetropolis algorithmadaptive sweep strategiesrandom proposal distributions

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Numerical analysis or methods applied to Markov chains (65C40)

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Cites Work

• Adaptive proposal distribution for random walk Metropolis algorithm
• Optimizing random scan Gibbs samplers
• Ordering and improving the performance of Monte Carlo Markov chains.
• Bayesian computation and stochastic systems. With comments and reply.
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Adaptive Rejection Metropolis Sampling within Gibbs Sampling
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• An adaptive Metropolis algorithm
• Bayesian survival analysis
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