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Bayesian analysis for simulation input and output

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Stephen E. ChickAuthors Info & Claims

WSC '97: Proceedings of the 29th conference on Winter simulation

Pages 253 - 260

https://doi.org/10.1145/268437.268488

Published: 01 December 1997 Publication History

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Cited By

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Li KJin XJia QRen DXia H(2024)An OCBA-Based Method for Efficient Sample Collection in Reinforcement LearningIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.328225721:3(3615-3626)Online publication date: Jul-2024
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Muñoz D(2023)Estimation of Expectations and Variance Components in Two-Level Nested Simulation ExperimentsAppliedMath10.3390/appliedmath30300313:3(582-600)Online publication date: 7-Aug-2023
https://doi.org/10.3390/appliedmath3030031
Zhang MHe YLiu GDai S(2023)Input Uncertainty Quantification Via Simulation Bootstrapping2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407968(3693-3704)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10407968
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Index Terms

Bayesian analysis for simulation input and output
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation evaluation

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Published In

WSC '97: Proceedings of the 29th conference on Winter simulation

December 1997

1452 pages

ISBN:078034278X

Editors:
Sigrún Andradóttir
Georgia Institute of Technology, Atlanta
,
Kevin J. Healy
Thread Technologies, West Lafayette, IN
,
David H. Withers
Dell Computer Co., Round Rock, TX
,
Barry L. Nelson
Northwestern Univ., Evanston, IL

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IEEE Computer Society

United States

Publication History

Published: 01 December 1997

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WSC97: 1997 Winter Simulation Conference

December 7 - 10, 1997

Georgia, Atlanta, USA

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WSC '97 Paper Acceptance Rate 121 of 191 submissions, 63%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Li KJin XJia QRen DXia H(2024)An OCBA-Based Method for Efficient Sample Collection in Reinforcement LearningIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.328225721:3(3615-3626)Online publication date: Jul-2024
https://doi.org/10.1109/TASE.2023.3282257
Muñoz D(2023)Estimation of Expectations and Variance Components in Two-Level Nested Simulation ExperimentsAppliedMath10.3390/appliedmath30300313:3(582-600)Online publication date: 7-Aug-2023
https://doi.org/10.3390/appliedmath3030031
Zhang MHe YLiu GDai S(2023)Input Uncertainty Quantification Via Simulation Bootstrapping2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407968(3693-3704)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10407968
Zhang JZhou M(2023)Ordinal OptimizationLearning Automata and Their Applications to Intelligent Systems10.1002/9781394188536.ch5(123-173)Online publication date: 17-Nov-2023
https://doi.org/10.1002/9781394188536.ch5
Herazo-Padilla NAugusto VDalmas BXie XBongue B(2022)A Decision-Tree-Based Bayesian Approach for Chance-Constrained Health Prevention Budget RationingIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.306980019:3(2322-2338)Online publication date: Jul-2022
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Corlu CBiller BTayur S(2018)Driving inventory system simulations with limited demand data: Insights from the newsvendor problemJournal of Simulation10.1080/17477778.2018.1488935(1-12)Online publication date: 10-Jul-2018
https://doi.org/10.1080/17477778.2018.1488935
Cheng R(2017)History of input modelingProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242194(1-21)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242194
Yi YXie W(2017)An Efficient Budget Allocation Approach for Quantifying the Impact of Input Uncertainty in Stochastic SimulationACM Transactions on Modeling and Computer Simulation10.1145/312914827:4(1-23)Online publication date: 27-Oct-2017
https://dl.acm.org/doi/10.1145/3129148
Song ENelson B(2017)Input Model RiskAdvances in Modeling and Simulation10.1007/978-3-319-64182-9_5(63-80)Online publication date: 29-Aug-2017
https://doi.org/10.1007/978-3-319-64182-9_5
Ryzhov IChen Y(2017)Bayesian Belief Models in Simulation-Based Decision-MakingAdvances in Modeling and Simulation10.1007/978-3-319-64182-9_10(181-217)Online publication date: 29-Aug-2017
https://doi.org/10.1007/978-3-319-64182-9_10
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Multiple input and multiple output simulation metamodeling using Bayesian networks

Input-Output Tools: A Language Facility for Interactive and Real-Time Systems

Multivariate Input Uncertainty in Output Analysis for Stochastic Simulation

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Multiple input and multiple output simulation metamodeling using Bayesian networks

Input-Output Tools: A Language Facility for Interactive and Real-Time Systems

Multivariate Input Uncertainty in Output Analysis for Stochastic Simulation

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