Bayesian Analysis

On Bayesian A- and D-Optimal Experimental Designs in Infinite Dimensions

Alen Alexanderian, Philip J. Gloor, and Omar Ghattas

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We consider Bayesian linear inverse problems in infinite-dimensional separable Hilbert spaces, with a Gaussian prior measure and additive Gaussian noise model, and provide an extension of the concept of Bayesian D-optimality to the infinite-dimensional case. To this end, we derive the infinite-dimensional version of the expression for the Kullback–Leibler divergence from the posterior measure to the prior measure, which is subsequently used to derive the expression for the expected information gain. We also study the notion of Bayesian A-optimality in the infinite-dimensional setting, and extend the well known (in the finite-dimensional case) equivalence of the Bayes risk of the MAP estimator with the trace of the posterior covariance, for the Gaussian linear case, to the infinite-dimensional Hilbert space case.

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Bayesian Anal., Volume 11, Number 3 (2016), 671-695.

First available in Project Euclid: 26 August 2015

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Bayesian inference in Hilbert space Gaussian measure Kullback–Leibler divergence Bayesian optimal experimental design expected information gain Bayes risk


Alexanderian, Alen; Gloor, Philip J.; Ghattas, Omar. On Bayesian A- and D-Optimal Experimental Designs in Infinite Dimensions. Bayesian Anal. 11 (2016), no. 3, 671--695. doi:10.1214/15-BA969.

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