Please use this identifier to cite or link to this item: https://doi.org/10.1137/20m1345517
Title: A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation
Authors: Xie, Wei
LI CHENG 
Wu, Yuefeng
Zhang, Pu
Keywords: nonparametric Bayesian approach
design of experiments
stochastic simulation
Uncertainty quantification
input uncertainty
Dirichlet process mixtures
Issue Date: Jan-2021
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Citation: Xie, Wei, LI CHENG, Wu, Yuefeng, Zhang, Pu (2021-01). A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation. SIAM/ASA Journal on Uncertainty Quantification 9 (4) : 1527-1552. ScholarBank@NUS Repository. https://doi.org/10.1137/20m1345517
Abstract: When we use simulation to assess the performance of stochastic systems, the input models used to drive simulation experiments are often estimated from finite real-world data. There exist both input model and simulation estimation uncertainties in the system performance estimates. Without strong prior information on the input models and the system mean response surface, in this paper, we pro pose a Bayesian nonparametric framework to quantify the impact from both sources of uncertainty. Specifically, since the real-world data often represent the variability caused by various latent sources of uncertainty, Dirichlet process mixture (DPM) based nonparametric input models are introduced to model a mixture of heterogeneous distributions, which can faithfully capture the important fea tures of real-world data, such as multimodality and skewness. Bayesian posteriors of flexible input models characterize the input model estimation uncertainty, which automatically accounts for both model selection and parameter value uncertainty. Then input model estimation uncertainty is prop agated to outputs by using direct simulation. Thus, under very general conditions, our framework delivers an empirical credible interval accounting for both input and simulation uncertainties. A variance decomposition is further developed to quantify the relative contributions from both sources of uncertainty. Our approach is supported by rigorous theoretical and empirical study.
Source Title: SIAM/ASA Journal on Uncertainty Quantification
URI: https://scholarbank.nus.edu.sg/handle/10635/217276
ISSN: 2166-2525
DOI: 10.1137/20m1345517
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