Pseudo-perfect and adaptive variants of the Metropolis–Hastings algorithm with an independent candidate density

DOI10.1080/00949650410001729463zbMath1075.65013OpenAlexW2060288690MaRDI QIDQ5460693

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Publication date: 18 July 2005

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650410001729463

zbMATH Keywords

numerical examplesinvariant measureMetropolis-Hastings algorithmexact samplingcoupling from the pastperfect samplingbackward coupling

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Numerical analysis or methods applied to Markov chains (65C40) Continuous-time Markov processes on discrete state spaces (60J27)

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Cites Work

• Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes
• Perfect sampling from independent Metropolis-Hastings chains
• Markov chains for exploring posterior distributions. (With discussion)
• Rates of convergence of the Hastings and Metropolis algorithms
• Langevin-type models II: Self-targeting candidates for MCMC algorithms
• Perfect Simulation of Conditionally Specified Models
• Exact Sampling from a Continuous State Space
• Perfect simulation and backward coupling^∗
• Exact sampling with coupled Markov chains and applications to statistical mechanics
• Simulating the Invariant Measures of Markov Chains Using Backward Coupling at Regeneration Times
• Monte Carlo sampling methods using Markov chains and their applications

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