Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments

By Peter Z. G. Qian, WU C.F.Jeff. Technometrics, 2008

388 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/711bc3ac0a82e30ae32617f706189d38050cfbef

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In Computational Theory and Mathematics

In Management Science and Operations Research

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Citations in the corpus

Recent Advances in Bayesian Optimization (2022, methodology): “Later, a Bayesian hierarchical GP model is developed in [147] to account for complex scale changes from low fidelity to high fidelity.”
Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision (2013, methodology): “This situation has been addressed in the multifidelity literature [see Kennedy and O’Hagan (2000) and Qian and Wu (2008) for modeling; Forrester, Sóbester, and Keane (2007) and Huang et al. (2006) for optimization] and is not considered here, although many of the concepts presented here may apply…”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, methodology): “Taking the contemporary bBayesian interpretation [Rasmussen and Williams (2006)] of ordinary kriging [Matheron (1969)], y is assumed to be a realization of a Gaussian process (GP) Y with unknown constant mean μ and known covariance kernel k.”
Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012, neutral): “A Bayesian approac h is considered in references [2, 16, 23], which model the system as”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “…function based modeling (Han et al. 2013; Zhou et al. 2017) and Bayesian multi-fidelity modeling (also called Co-Kriging) (Kennedy and O ’ Hagan 2000, 2001; Forrester et al. 2007; Qian and Wu 2008), have gained popularity in multidisciplinary design, optimization and uncertainty quantification.”
Performance study of multi-fidelity gradient enhanced kriging (2015, methodology): “An autoregressive formulation based multi-fidelity modelling is introduced by Kennedy and O’Hagan (2000) and is quoted to be very efficient by Forrester et al. (2007) and Qian and Wu (2008).”
Learning about physical parameters: the importance of model discrepancy (2014, methodology): “Since then, their inferential ideas and modeling framework have been widely adopted and further developed, e.g. Higdon et al (2004, 2008), Qian and Wu (2008), Goldstein and Rougier (2009), Bayarri et al (2007), Gramacy and Lee (2008).”
Review of multi-fidelity models (2016, methodology): “Accounting for these correlations can improve the model’s predictive performance, making the model more complex and computationally intensive [174, 131, 69].”