Variance reduction techniques for the simulation of Markov process. II: Matrix iterative methods

DOI10.1007/BF00288533zbMath0407.60079OpenAlexW1575673157MaRDI QIDQ1258138

Philip Heidelberger

Publication date: 1980

Published in: Acta Informatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00288533

zbMATH Keywords

MarkQueueing ModelReducing the Variance of Simulation Estimates

Mathematics Subject Classification ID

Queueing theory (aspects of probability theory) (60K25) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (1)

Variance reduction techniques for the simulation of Markov process. II: Matrix iterative methods

Cites Work

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• Variance reduction techniques for the simulation of Markov process. II: Matrix iterative methods
• Variance Reduction Techniques for the Simulation of Markov Processes, I: Multiple Estimates
• Statistical Results on Control Variables with Application to Queueing Network Simulation
• Simulating Stable Stochastic Systems: III. Regenerative Processes and Discrete-Event Simulations
• Discrete time methods for simulating continuous time Markov chains
• Concomitant Control Variables Applied to the Regenerative Simulation of Queuing Systems
• A Retrospective and Prospective Survey of the Monte Carlo Method

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