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This paper discusses some of the merits of the strong approximation ideas in developing diffusion approximations for queueing systems. Letting ρ be the utilization of the server, it is well known that...
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling gener...
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
We set the context for capital approximation within the framework of the Basel II / III regulatory capital accords. This is particularly topical as the Basel III accord is shortly due to take effect. ...
We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of specia...
For the detection and visualization of trends, periodicities, local peculiarities in measurement series, the methods of fractal and wavelet-analysis are of considerable current use.One of such method...
We consider the synthesis problem of Compressed Sensing –given s and an M×n matrix A, extract from it an m × n submatrix Am, certified to be s-good, with m as small as possible. Starting from the ve...
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated with outliers....
We prove that Le Cam's theorem for compound Poisson can be extended to a much larger set of compound distributions. Signed compound negative binomial measure is introduced. Relations between compou...
We consider finite-dimensional multivalued stochastic differential equations where the drift has a multivalued and monotone term. Existence and approximation results are obtained by an existence th...
A refined estimate of the rate of convergence of one-dimensional distributions of nonrandomly centered generalized Cox processes to location mixtures of normal laws is presented. Asymp- . totic exp...
In this paper we approximate risk process by an a-stable Uvy motion (1 < a < 2). We consider two conditions imposed on the value of the premium rate. The first one assumes that the premiums exceed ...

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