publication . Conference object . 2011

Optimal covariance selection for estimation using graphical models

Sergey Vichik; Yaakov Oshman;
Open Access
  • Published: 01 Aug 2011
Abstract
We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditional independence constraints, how should one select the constrained covariance, optimally representing the (given) a priori covariance, but also satisfying the constraints? In...
Subjects
ACM Computing Classification System: MathematicsofComputing_NUMERICALANALYSIS
free text keywords: Statistics, Covariance matrix, Rational quadratic covariance function, Estimation of covariance matrices, Matérn covariance function, Law of total covariance, Covariance function, Control theory, Covariance, Covariance intersection, Mathematical optimization, Computer science
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publication . Conference object . 2011

Optimal covariance selection for estimation using graphical models

Sergey Vichik; Yaakov Oshman;