Importance Sampling for the Simulation of Highly Reliable Markovian Systems

DOI10.1287/mnsc.40.3.333zbMath0805.90049OpenAlexW2164863330MaRDI QIDQ4302651

Perwez Shahabuddin

Publication date: 4 October 1994

Published in: Management Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/mnsc.40.3.333

zbMATH Keywords

reliabilityMarkov chainsvariance reductionstochastic simulationavailabilitymean time to failureimportance sampling techniqueshighly reliable componentsfailure biasingsimulation of Markovian systems

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Reliability, availability, maintenance, inspection in operations research (90B25) Applications of Markov renewal processes (reliability, queueing networks, etc.) (60K20)

Related Items (11)

Representation of analysis results involving aleatory and epistemic uncertainty ⋮ Some Recent Results in Rare Event Estimation ⋮ Command-based importance sampling for statistical model checking ⋮ Counterexamples in importance sampling for large deviations probabilities ⋮ The cross-entropy method with patching for rare-event simulation of large Markov chains ⋮ Optimal importance sampling for Markovian systems with applications to tandem queues ⋮ Approximating zero-variance importance sampling in a reliability setting ⋮ Importance sampling simulations of Markovian reliability systems using cross-entropy ⋮ Dynamic importance sampling for uniformly recurrent Markov chains ⋮ Spare parts allocation by improved genetic algorithm and Monte Carlo simulation ⋮ A Cross-Entropy Scheme for Mixtures

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