Article

Bayesian ideas and discrete event simulation: why, what and how

Author:

Stephen E. ChickAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 96 - 105

Published: 03 December 2006 Publication History

Abstract

Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the performance of systems, or about inputs parameters of those systems. They also can be used to improve the efficiency of discrete optimization with simulation and response surface methods. Bayesian methods work well with other decision theoretic tools, and can therefore provide a link from traditional operations-level experiments to higher-level managerial decision-making needs, in addition to improving the efficiency of computer experiment

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Lam H(2023)Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty ApplicationsProceedings of the Winter Simulation Conference10.5555/3643142.3643266(1501-1515)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643266
Bai YLam H(2020)Calibrating input parameters via eligibility setsProceedings of the Winter Simulation Conference10.5555/3466184.3466425(2114-2125)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466425
Corlu CPanadero JJuan AOnggo B(2020)On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problemsProceedings of the Winter Simulation Conference10.5555/3466184.3466424(2105-2113)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466424
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Bayesian ideas and discrete event simulation: why, what and how
1. Computing methodologies
  1. Modeling and simulation
2. Mathematics of computing
  1. Probability and statistics
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Published In

cover image ACM Conferences

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

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IIE: Institute of Industrial Engineers
ASA: American Statistical Association
IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE-CS\DATC: The IEEE Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International
INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

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Winter Simulation Conference

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Published: 03 December 2006

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WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

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

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Cited By

Lam H(2023)Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty ApplicationsProceedings of the Winter Simulation Conference10.5555/3643142.3643266(1501-1515)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643266
Bai YLam H(2020)Calibrating input parameters via eligibility setsProceedings of the Winter Simulation Conference10.5555/3466184.3466425(2114-2125)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466425
Corlu CPanadero JJuan AOnggo B(2020)On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimisation problemsProceedings of the Winter Simulation Conference10.5555/3466184.3466424(2105-2113)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466424
Lam HQian HSon YHaas P(2019)Random perturbation and bagging to quantify input uncertaintyProceedings of the Winter Simulation Conference10.5555/3400397.3400423(320-331)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400423
Glynn PLam HJohansson BJain S(2018)Constructing simulation output intervals under input uncertainty via data sectioningProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320707(1551-1562)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320707
Gao SXiao HZhou EChen WHuschka TChick S(2016)Optimal computing budget allocation with input uncertaintyProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042210(839-846)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042210
Lam HHuschka TChick S(2016)Advanced tutorialProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042128(178-192)Online publication date: 11-Dec-2016
https://dl.acm.org/doi/10.5555/3042094.3042128
Zhang QSong Y(2015)Simulation selection for empirical model comparisonProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2889124(3777-3788)Online publication date: 6-Dec-2015
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Xie JFrazier PHill RKuhl M(2013)Upper bounds on the Bayes-optimal procedure for ranking & selection with independent normal priorsProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676095(877-887)Online publication date: 8-Dec-2013
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Frazier PRose OUhrmacher A(2012)Optimization via simulation with Bayesian statistics and dynamic programmingProceedings of the Winter Simulation Conference10.5555/2429759.2429767(1-16)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2429767
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