Simulating the Invariant Measures of Markov Chains Using Backward Coupling at Regeneration Times

DOI10.1017/S0269964800005209zbMath0956.65004OpenAlexW2000294345MaRDI QIDQ4950715

Jem N. Corcoran, Richard L. Tweedie, Sergeĭ Georgievich Foss

Publication date: 9 April 2000

Published in: Probability in the Engineering and Informational Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0269964800005209

zbMATH Keywords

algorithmrandom samplesinvariant measureMarkov chainbackward couplingstochastically monotone chainscycle-length truncation

Mathematics Subject Classification ID

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

Related Items

Perfect sampling of ergodic Harris chains ⋮ Perfect sampling for Bayesian variable selection in a linear regression model ⋮ Limit theorems for a random directed slab graph ⋮ Relating Time and Customer Averages for Queues Using ‘forward’ Coupling from the Past ⋮ Backward coupling in bounded free-choice nets under Markovian and non-Markovian assumptions ⋮ Pseudo-perfect and adaptive variants of the Metropolis–Hastings algorithm with an independent candidate density ⋮ Perfect simulation of processes with long memory: A “coupling into and from the past” algorithm ⋮ An interruptible algorithm for perfect sampling via Markov chains ⋮ PERFECT STOCHASTIC SUMMATION IN HIGH ORDER FEYNMAN GRAPH EXPANSIONS ⋮ Perfect sampling from independent Metropolis-Hastings chains

Cites Work

• Unnamed Item
• Two ergodicity criteria for stochastically recursive sequences
• Computable bounds for geometric convergence rates of Markov chains
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
• Geometric Convergence Rates for Stochastically Ordered Markov Chains