Fast Estimation of Expected Information Gain for Bayesian Experimental Design Based on Laplace Approximation

Handle URI:
http://hdl.handle.net/10754/623976
Title:
Fast Estimation of Expected Information Gain for Bayesian Experimental Design Based on Laplace Approximation
Authors:
Long, Quan ( 0000-0002-0329-9437 ) ; Scavino, Marco ( 0000-0001-5114-853X ) ; Tempone, Raul ( 0000-0003-1967-4446 ) ; Wang, Suojin
Abstract:
Shannon-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.
KAUST Department:
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)
Issue Date:
6-Jan-2014
Type:
Poster
Appears in Collections:
Posters; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

Full metadata record

DC FieldValue Language
dc.contributor.authorLong, Quanen
dc.contributor.authorScavino, Marcoen
dc.contributor.authorTempone, Raulen
dc.contributor.authorWang, Suojinen
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.en
dc.subjectBayesianen
dc.titleFast Estimation of Expected Information Gain for Bayesian Experimental Design Based on Laplace Approximationen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateJanuary 6-10, 2014en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)en
dc.conference.locationKAUSTen
dc.contributor.institutionTexas A&M Universityen
kaust.authorLong, Quanen
kaust.authorScavino, Marcoen
kaust.authorTempone, Raulen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.