Campuses:

covariance approximation

Friday, September 8, 2017 - 10:40am - 11:15am
Luis Tenorio (Colorado School of Mines)
Since in Bayesian inversion data are often informative only on a low-dimensional subspace of the parameter space,
significant computational savings can be achieved using such subspace to characterize and approximate the posterior distribution of the parameters.
We study approximations of the posterior covariance matrix defined as low-rank updates of the prior covariance matrix and
prove their optimality for a broad class of loss functions which includes the Forstner
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