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International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029282
pages 295-310


Tiangang Cui
School of Mathematical Sciences, Monash University, VIC 3800, Australia
C. Fox
Department of Physics, University of Otago, Dunedin 9016, New Zealand
G. K. Nicholls
Department of Statistics, University of Oxford, Oxford, OX1 3LG, United Kingdom
M. J. O'Sullivan
Department of Engineering Sciences, The University of Auckland, Auckland 1010, New Zealand

Краткое описание

We introduce a parallel rejection scheme to give a simple but reliable way to parallelize the Metropolis-Hastings algorithm. This method can be particularly useful when the target density is computationally expensive to evaluate and the acceptance rate of the Metropolis-Hastings is low. We apply the resulting method to quantify uncertainties of inverse problems, in which we aim to calibrate a challenging nonlinear geothermal reservoir model using real measurements from well tests. We demonstrate the parallelized method on various well-test scenarios. In some scenarios, the sample-based statistics obtained by our scheme shows clear advantages in providing robust model calibration and prediction compared with those obtained by nonlinear optimization methods.


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