Titlebook: Supercomputer Simulations in Chemistry; Proceedings of the S M. Dupuis Conference proceedings 1986 Springer-Verlag Berlin Heidelberg 1986 A

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P. O. Löwdinand processing. The technique making computation with several hundred dimensional probability distribution possible was suggested by Lauritzen and Spiegelhalter. However, to employ it one has to transform a Bayesian network into a .. This is because decomposable models (or more precisely their build

Exploit

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W. Kutzelnigg,M. Schindler,W. Klopper,S. Koch,U. Meier,H. WallmeierLipschitz functions in an arbitrary number of dimensions. We exhibit optimal or nearly optimal algorithms, i.e., algorithms which match or are close to the lower bounds on the cost of solving these problems..In Section 1 we describe a Fixed Point Envelope (FPE) algorithm which we have shown to be th

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Cursory

Martyn F. Guest by . where ..(.) is the nominal polynomial of .-th order, . = [q.,q.,... ..]. is a vector of perturbation parameters, ..(.) are linear functions of ., . is a prescribed polytope in .. containing the origin, and . ≥ 0 is a parameter which controls the size of . Given an open set . in the complex pla

OVERT

E. Clementi,S. Chin,D. Logan are not always fully appreciated. Essentially, a pattern recognition task boils down to measuring the distance between a physical representation of a new, as yet unknown token, and all elements of a set of preexisting patterns, of course in the same physical representation. On the one hand, the ‘pa

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Berni J. Alder presents new robust forecasting procedures.Presentation of .Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.),