A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation

Wei Xie (), Barry L. Nelson () and Russell R. Barton ()
Additional contact information
Wei Xie: Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Russell R. Barton: Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802

Operations Research, 2014, vol. 62, issue 6, 1439-1452

Abstract: When we use simulation to estimate the performance of a stochastic system, the simulation often contains input models that were estimated from real-world data; therefore, there is both simulation and input uncertainty in the performance estimates. In this paper, we provide a method to measure the overall uncertainty while simultaneously reducing the influence of simulation estimation error due to output variability. To reach this goal, a Bayesian framework is introduced. We use a Bayesian posterior for the input-model parameters, conditional on the real-world data, to quantify the input-parameter uncertainty; we propagate this uncertainty to the output mean using a Gaussian process posterior distribution for the simulation response as a function of the input-model parameters, conditional on a set of simulation experiments. We summarize overall uncertainty via a credible interval for the mean. Our framework is fully Bayesian, makes more effective use of the simulation budget than other Bayesian approaches in the stochastic simulation literature, and is supported with both theoretical analysis and an empirical study. We also make clear how to interpret our credible interval and why it is distinctly different from the confidence intervals for input uncertainty obtained in other papers.

Keywords: input uncertainty; Bayesian inference; credible interval; simulation output analysis; metamodel; Gaussian process (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2014.1316 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:62:y:2014:i:6:p:1439-1452

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().