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dc.contributor.authorBen Issaid, Chaouki
dc.contributor.authorLong, Quan
dc.contributor.authorScavino, Marco
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-06-05T08:35:47Z
dc.date.available2017-06-05T08:35:47Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624067
dc.description.abstractExperimental design is very important since experiments are often resource-exhaustive and time-consuming. We carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data for our purpose. One of the major difficulties in evaluating the expected information gain is that the integral is nested and can be high dimensional. We propose using Multilevel Monte Carlo techniques to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, the Multilevel Monte Carlo can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the Multilevel Monte Carlo method imposes less assumptions, such as the concentration of measures, required by Laplace method. We test our Multilevel Monte Carlo technique using a numerical example on the design of sensor deployment for a Darcy flow problem governed by one dimensional Laplace equation. We also compare the performance of the Multilevel Monte Carlo, Laplace approximation and direct double loop Monte Carlo.
dc.titleBayesian Optimal Experimental Design Using Multilevel Monte Carlo
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
kaust.personBen Issaid, Chaouki
kaust.personLong, Quan
kaust.personScavino, Marco
kaust.personTempone, Raul
refterms.dateFOA2018-06-14T03:20:25Z


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