Regenerative Markov Chain Monte Carlo for Any Distribution

DOI10.1080/03610918.2011.615433zbMath1254.65011OpenAlexW2058798910MaRDI QIDQ3168385

David D. L. Minh, Do Le Minh (Paul), Andrew L. Nguyen

Publication date: 30 October 2012

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.615433

zbMATH Keywords

algorithmMarkov chain Monte Carlo methodnumerical examplesregenerative simulation

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (6)

Bayesian networks: regenerative Gibbs samplings ⋮ Bayesian networks: generating independent samples ⋮ Regeneration-enriched Markov processes with application to Monte Carlo ⋮ Layer Sampling ⋮ Regenerative Markov Chain Importance Sampling ⋮ Understanding the Hastings Algorithm

Cites Work

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• Markov chains for exploring posterior distributions. (With discussion)
• Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution
• Markov Chains and Stochastic Stability
• Introduction to Regenerative Simulation
• Simulating Stable Stochastic Systems: III. Regenerative Processes and Discrete-Event Simulations
• A splitting technique for Harris recurrent Markov chains
• On the applicability of regenerative simulation in Markov chain Monte Carlo
• Exact sampling with coupled Markov chains and applications to statistical mechanics
• Monte Carlo sampling methods using Markov chains and their applications
• Introducing Monte Carlo Methods with R

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