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International Journal for Uncertainty Quantification
インパクトファクター: 4.911 5年インパクトファクター: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2013005679
pages 111-132

DATA-FREE INFERENCE OF UNCERTAIN PARAMETERS IN CHEMICAL MODELS

Habib N. Najm
Sandia National Laboratories P.O. Box 969, MS 9051, Livermore, CA 94551, USA
Robert D. Berry
P.O.Box 969, MS 9051; Sandia National Laboratories, Livermore, California 94551, USA
Cosmin Safta
P.O.Box 969, MS 9051; Computational Science and Analysis, Sandia National Laboratories, Livermore, CA, 94550
Khachik Sargsyan
Sandia National Laboratories, Livermore, CA, USA
Bert J. Debusschere
P.O.Box 969, MS 9051; Sandia National Laboratories, Livermore, California 94551, USA

要約

We outline the use of a data-free inference procedure for estimation of uncertain model parameters for a chemical model of methane-air ignition. The method involves a nested pair of Markov chains, exploring both the data and parametric spaces, to discover a pooled joint posterior consistent with available information. We describe the highlights of the method, and detail its particular implementation in the system at hand. We examine the performance of the procedure, focusing on the robustness and convergence of the estimated joint parameter posterior with increasing number of data chain samples. We also comment on comparisons of this posterior with the missing reference posterior density.


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