Unbiased metamodeling via likelihood ratios

Jing Dong, M. Ben Feng, Barry L Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Metamodeling has been a topic of longstanding interest in stochastic simulation because of the usefulness of metamodels for optimization, sensitivity, and real- or near-real-time decision making. Experiment design is the foundation of classical metamodeling: an effective experiment design uncovers the spatial relationships among the design/decision variables and the simulation response; therefore, more design points, providing better coverage of space, is almost always better. However, metamodeling based on likelihood ratios (LRs) turns the design question on its head: each design point provides an unbiased prediction of the response at any other location in space, but perhaps with such inflated variance as to be counterproductive. Thus, the question becomes more which design points to employ for prediction and less where to place them. In this paper we take the first comprehensive look at LR metamodeling, categorizing both the various types of LR metamodels and the contexts in which they might be employed.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1778-1789
Number of pages12
ISBN (Electronic)9781538665725
DOIs
StatePublished - Jan 31 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
Volume2018-December
ISSN (Print)0891-7736

Conference

Conference2018 Winter Simulation Conference, WSC 2018
CountrySweden
CityGothenburg
Period12/9/1812/12/18

Fingerprint

Metamodeling
Likelihood Ratio
Metamodel
Prediction
Stochastic Simulation
Design
Experiment
Coverage
Decision making
Decision Making
Experiments
Real-time
Optimization

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Dong, J., Ben Feng, M., & Nelson, B. L. (2019). Unbiased metamodeling via likelihood ratios. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause (pp. 1778-1789). [8632506] (Proceedings - Winter Simulation Conference; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2018.8632506
Dong, Jing ; Ben Feng, M. ; Nelson, Barry L. / Unbiased metamodeling via likelihood ratios. WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1778-1789 (Proceedings - Winter Simulation Conference).
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Dong, J, Ben Feng, M & Nelson, BL 2019, Unbiased metamodeling via likelihood ratios. in WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause., 8632506, Proceedings - Winter Simulation Conference, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 1778-1789, 2018 Winter Simulation Conference, WSC 2018, Gothenburg, Sweden, 12/9/18. https://doi.org/10.1109/WSC.2018.8632506

Unbiased metamodeling via likelihood ratios. / Dong, Jing; Ben Feng, M.; Nelson, Barry L.

WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1778-1789 8632506 (Proceedings - Winter Simulation Conference; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Dong J, Ben Feng M, Nelson BL. Unbiased metamodeling via likelihood ratios. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1778-1789. 8632506. (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2018.8632506