Simulation run length planning for stochastic loss models
We derive approximate formulas for the asymptotic variance of estimators of the steady-state blocking probability in a multi-server loss system. These formulas can be used to predict simulation run lengths required to obtain desired statistical precision before the simulation has been run, which can...
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| Veröffentlicht in: | Proceedings of the 27th conference on Winter simulation S. 1384 - 1391 |
|---|---|
| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
Washington, DC, USA
IEEE Computer Society
01.12.1995
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| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Applied computing
> Computers in other domains
> Personal computers and PC applications
> Computer games
Information systems
> Information systems applications
> Multimedia information systems
> Massively multiplayer online games
|
| ISBN: | 0780330188, 9780780330184 |
| Online-Zugang: | Volltext |
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| Abstract | We derive approximate formulas for the asymptotic variance of estimators of the steady-state blocking probability in a multi-server loss system. These formulas can be used to predict simulation run lengths required to obtain desired statistical precision before the simulation has been run, which can aid in the design of simulation experiments. It is natural to delete an initial portion of the simulation run to allow the system to approach steady state when it starts out empty. As the system size increases, the time to approach steady state becomes a greater portion of the overall simulation time as system size increases. |
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| AbstractList | We derive approximate formulas for the asymptotic variance of estimators of the steady-state blocking probability in a multi-server loss system. These formulas can be used to predict simulation run lengths required to obtain desired statistical precision before the simulation has been run, which can aid in the design of simulation experiments. It is natural to delete an initial portion of the simulation run to allow the system to approach steady state when it starts out empty. As the system size increases, the time to approach steady state becomes a greater portion of the overall simulation time as system size increases. |
| Author | Whitt, Ward Srikant, Rayadurgam |
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| Editor | Alexopoulos, Christos Kang, Keebom |
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| EndPage | 1391 |
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| Snippet | We derive approximate formulas for the asymptotic variance of estimators of the steady-state blocking probability in a multi-server loss system. These formulas... |
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| SubjectTerms | Applied computing -- Computers in other domains -- Personal computers and PC applications -- Computer games Computing methodologies -- Machine learning Computing methodologies -- Modeling and simulation Information systems -- Information systems applications -- Multimedia information systems -- Massively multiplayer online games Mathematics of computing -- Mathematical analysis -- Functional analysis -- Approximation Mathematics of computing -- Mathematical analysis -- Numerical analysis Mathematics of computing -- Probability and statistics Software and its engineering -- Software organization and properties -- Contextual software domains -- Virtual worlds software -- Interactive games Theory of computation -- Design and analysis of algorithms -- Approximation algorithms analysis Theory of computation -- Theory and algorithms for application domains -- Algorithmic game theory and mechanism design |
| Title | Simulation run length planning for stochastic loss models |
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