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Computing the distribution function of a conditional expectation via Monte Carlo: discrete conditioning spaces

Authors:

Shing-Hoi Lee,

Peter W. GlynnAuthors Info & Claims

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2

Pages 1654 - 1663

https://doi.org/10.1145/324898.325356

Published: 01 December 1999 Publication History

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References

[1]

Billingsley, P. 1995. Probability and Measure 3rd-ed. New York: John Wiley and Sons.

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Bucklew, J. 1990. Large Deviation Techniques in Decision, Simulation and Estimation. New York: John Wiley and Sons.

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Duffie, D. 1996. Dynamic Asset Pricing Theory. 2nd ed. Princeton University Press, Princeton, New Jersey.

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Lee, S.H. and Glynn, P.W. 1999. Computing the Distribution Function of a Conditional Expectation via Monte Carlo. Submitted for publication.

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Cited By

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Xie WNelson BBarton R(2014)A Bayesian Framework for Quantifying Uncertainty in Stochastic SimulationOperations Research10.1287/opre.2014.131662:6(1439-1452)Online publication date: Dec-2014
https://doi.org/10.1287/opre.2014.1316
Barton RRose OUhrmacher A(2012)Input uncertainty in outout analysisProceedings of the Winter Simulation Conference10.5555/2429759.2429766(1-12)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2429766
Chick SNicol DFujimoto R(2006)Bayesian ideas and discrete event simulationProceedings of the 38th conference on Winter simulation10.5555/1218112.1218134(96-105)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218134
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Index Terms

Computing the distribution function of a conditional expectation via Monte Carlo: discrete conditioning spaces
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Discrete-event simulation
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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Published In

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2

December 1999

1754 pages

ISBN:0780357809

DOI:10.1145/324898

Editors:
Phillip A. Farrington
Univ. of Alabama in Huntsville, Huntsville
,
Harriet Black Nembhard
Univ. of Wisconsin-Madison, Madison
,
Gerald W. Evans
Univ. of Louisville, Louisville, KY
,
David T. Sturrock
Systems Modeling, Searickley, PA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1999

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WSC99

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WSC99: Winter Simulation Conference 1999

December 5 - 8, 1999

Arizona, Phoenix, USA

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Cited By

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Xie WNelson BBarton R(2014)A Bayesian Framework for Quantifying Uncertainty in Stochastic SimulationOperations Research10.1287/opre.2014.131662:6(1439-1452)Online publication date: Dec-2014
https://doi.org/10.1287/opre.2014.1316
Barton RRose OUhrmacher A(2012)Input uncertainty in outout analysisProceedings of the Winter Simulation Conference10.5555/2429759.2429766(1-12)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2429766
Chick SNicol DFujimoto R(2006)Bayesian ideas and discrete event simulationProceedings of the 38th conference on Winter simulation10.5555/1218112.1218134(96-105)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218134
Chick S(2006)Bayesian Ideas and Discrete Event Simulation: Why, What and HowProceedings of the 2006 Winter Simulation Conference10.1109/WSC.2006.323042(96-106)Online publication date: Dec-2006
https://doi.org/10.1109/WSC.2006.323042
Chick S(2004)Bayesian methods for discrete event simulationProceedings of the 36th conference on Winter simulation10.5555/1161734.1161758(89-100)Online publication date: 5-Dec-2004
https://dl.acm.org/doi/10.5555/1161734.1161758
Chick S(2004)Bayesian Methods for Discrete Event SimulationProceedings of the 2004 Winter Simulation Conference, 2004.10.1109/WSC.2004.1371306(83-94)Online publication date: 2004
https://doi.org/10.1109/WSC.2004.1371306
Steckley SHenderson SFerrin DMorrice D(2003)Simulation input modelingProceedings of the 35th conference on Winter simulation: driving innovation10.5555/1030818.1030872(383-391)Online publication date: 7-Dec-2003
https://dl.acm.org/doi/10.5555/1030818.1030872
Staum JEhrlichman SLesnevski VFerrin DMorrice D(2003)New simulation methodology for financeProceedings of the 35th conference on Winter simulation: driving innovation10.5555/1030818.1030862(310-318)Online publication date: 7-Dec-2003
https://dl.acm.org/doi/10.5555/1030818.1030862
Henderson SFerrin DMorrice D(2003)Input model uncertainty: Why do we care and what should we do about it?Proceedings of the 35th conference on Winter simulation: driving innovation10.5555/1030818.1030833(90-100)Online publication date: 7-Dec-2003
https://dl.acm.org/doi/10.5555/1030818.1030833
Steckley SHenderson S(2003)A kernel approach to estimating the density of a conditional expectationProceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)10.1109/WSC.2003.1261447(383-391)Online publication date: 2003
https://doi.org/10.1109/WSC.2003.1261447
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