dc.contributor.author Ben Issaid, Chaouki dc.contributor.author Long, Quan dc.contributor.author Scavino, Marco dc.contributor.author Tempone, Raul dc.date.accessioned 2017-06-05T08:35:47Z dc.date.available 2017-06-05T08:35:47Z dc.date.issued 2015-01-07 dc.identifier.uri http://hdl.handle.net/10754/624067 dc.description.abstract Experimental 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.title Bayesian Optimal Experimental Design Using Multilevel Monte Carlo dc.type Poster dc.contributor.department Applied Mathematics and Computational Science Program dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Statistics Program dc.conference.date January 6-9, 2015 dc.conference.name Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015) dc.conference.location KAUST kaust.person Ben Issaid, Chaouki kaust.person Long, Quan kaust.person Scavino, Marco kaust.person Tempone, Raul refterms.dateFOA 2018-06-14T03:20:25Z
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