Regression metamodeling in simulation using Bayesian methods

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Regression metamodeling in simulation using Bayesian methods

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Winter Simulation Conference archive
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1 table of contents

Phoenix, Arizona, United States

Pages: 330 - 335

Year of Publication: 1999

ISBN:0-7803-5780-9

Author

Russell C. H. Cheng

Faculty of Mathematical Studies, University of Southampton, Highfield, Southampton, SO17 1BJ, England

Sponsors

ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 12, Citation Count: 8

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Cheng, R.C.H. and Amin, N.A.K. (1981). Maximum Like-lihood Estimation of Paramters in the Inverse Gaussian distribution, with Unknown Origin. Technometrics, 23, 257-263.
	2	Cheng, R.C.H. and Traylor, L. (1995). Non-regular max-imum likelihood problems (with discussion). Journal of the Royal Statistical Society, Series B, 57, 1, 3-44.
	3	Russel C. H. Cheng, Bayesian model selection when the number of components is unknown, Proceedings of the 30th conference on Winter simulation, p.653-660, December 13-16, 1998, Washington, D.C., United States
	4	Russell C. H. Cheng , Jack P. C. Kleijnen, Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses, Operations Research, v.47 n.5, p.762-777, May 1999 [doi>10.1287/opre.47.5.762]
	5	George, E.I. and McCulloch, R.E. (1993). Variable Selection via Gibbs sampling. J. Am. Statist. Ass. 85, 398-409.
	6	Gilks, W.R., Richardson, S. and Spiegelhalter, D.J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
	7	M. G. Kendall , A. Stuart , J. K. Ord, Kendall's advanced theory of statistics, Oxford University Press, Inc., New York, NY, 1987
	8	Young, A.S. (1977). A Bayesian approach to prediction using polynomials. Biometrika, 64, 309-317.

CITED BY 8

	Stephen E. Chick, Bayesian methods: bayesian methods for simulation, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Jason R. W. Merrick , Varun Dinesh , Amita Singh , J. René van Dorp , Thomas A. Mazzuchi, Simulation of large networks: propagation of uncertainty in a simulation-based maritime risk assessment model utilizing Bayesian simulation techniques, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
	Christos G. Panayiotou , Christos G. Cassandras , Wei-Bo Gong, Model abstraction for discrete event systems using neural networks and sensitivity information, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Stephen E. Chick, Bayesian methods for discrete event simulation, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
	Stephen E. Chick, Bayesian ideas and discrete event simulation: why, what and how, Proceedings of the 37th conference on Winter simulation, December 03-06, 2006, Monterey, California
	Russell C. H. Cheng , Christine S. M. Currie, Optimization by simulation metamodelling methods, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
	Isabel R. Santos , Pedro R. Santos, Simulation metamodels for modeling output distribution parameters, Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come, December 09-12, 2007, Washington D.C.
	Laigang Song , Xueping Li , Alberto Garcia-Diaz, Multi-echelon supply chain simulation using metamodel, Proceedings of the 40th Conference on Winter Simulation, December 07-10, 2008, Miami, Florida