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dc.contributor.authorLong, Quan
dc.contributor.authorScavino, Marco
dc.contributor.authorTempone, Raul
dc.contributor.authorWang, Suojin
dc.date.accessioned2017-06-01T10:20:42Z
dc.date.available2017-06-01T10:20:42Z
dc.date.issued2014-01-06
dc.identifier.urihttp://hdl.handle.net/10754/623976
dc.description.abstractShannon-type expected information gain is an important utility in evaluating the usefulness of a proposed experiment that involves uncertainty. Its estimation, however, cannot rely solely on Monte Carlo sampling methods, that are generally too computationally expensive for realistic physical models, especially for those involving the solution of stochastic partial differential equations. In this work we present a new methodology, based on the Laplace approximation of the posterior probability density function, to accelerate the estimation of expected information gain in the model parameters and predictive quantities of interest. Furthermore, in order to deal with the issue of dimensionality in a complex problem, we use sparse quadratures for the integration over the prior. We show the accuracy and efficiency of the proposed method via several nonlinear numerical examples, including a single parameter design of one dimensional cubic polynomial function and the current pattern for impedance tomography.
dc.subjectBayesian
dc.titleFast Estimation of Expected Information Gain for Bayesian Experimental Design Based on Laplace Approximation
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 6-10, 2014
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
dc.conference.locationKAUST
dc.contributor.institutionTexas A&M University
kaust.personLong, Quan
kaust.personScavino, Marco
kaust.personTempone, Raul
refterms.dateFOA2018-06-13T18:07:11Z


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