Constrained estimates of multipath covariance

Behaviour and Convergence of the Constrained Covariance

We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables...

Dynamic constrained multipath routing for MPLS networks

|Multipath routing employs multiple parallel paths between a traÆc source and destination in order to relax the most heavily congested link in Internet backbone. A large bandwidth path can be easily set up too. Although multipath routing is useful, the total network resources, i.e. sum of link bandwidths consumed, could be wasted when the acquired path is larger(in terms of number of hops) than...

Entropy-constrained halftoning using multipath tree coding

We suggest an optimization-based method for halftoning that involves looking ahead before a decision for each binary output pixel is made. We first define a mixture distortion criterion that is a combination of a frequency-weighted mean square error (MSE) and a measure depending on the distances between minority pixels in the halftone. A tree-coding approach with the ML-algorithm is used for mi...

Understanding Covariance Estimates in Expectation Propagation

Bayesian inference for most models of modern interest requires approximation of the posterior distribution. Variational inference (VI) methods formulate posterior approximation as a particular optimization problem and have grown in popularity due to their fast runtime on large datasets. There is recent work that suggests it might be beneficial to instead consider an alternative optimization obj...

Kernel Constrained Covariance for Dependence Measurement