Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

Clustering: from modeling to visualizing Mapping clusters as spherical Gaussians Numerical illustrations for functional data Discussion.. Take

The key point is to ex- plicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial

Berry–Esseen bounds, Breuer–Major CLT, Brownian sheet, fractional Brownian motion, local Edgeworth expansions, Malliavin calculus, multiple stochastic integrals, normal

Elimination Cost and Fill-in. When we eliminate columns, the number of non-zero elements of the current matrix usually changes. In sparse factorizations, the non-zero pattern of

Key words: Berry-Ess´een bounds; Breuer-Major CLT; Brownian sheet; Fractional Brownian motion; Local Edgeworth expansions; Malliavin calculus; Multiple stochastic integrals; Nor-

In such situations, where the low θ agents are worse off under private property, there is a trade-off between the insurance properties of the commons and the efficiency gains of

that enables us to compute the reduced echelon form in-place and in subcubic time (see Algorithm 8), while TRLUM will be used to derive a fast and in-place method for matrix products

An exact renormalization formula for Gaussian exponential sums and applications.. Alexander Fedotov,