Titlebook: New Perspectives on Approximation and Sampling Theory; Festschrift in Honor Ahmed I. Zayed,Gerhard Schmeisser Book 2014 Springer Internatio

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J. R. Higginsmental importance in financial mathematics, e.g. for the risk-neutral valuation formula (Black-Scholes formula) or the question of the hedgeability of options and the completeness of market models. Chapters on the valuation of options in complete and incomplete markets and on the determination of op

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Andi Kivinukk,Gert Tambergs a phase transition, and even includes some novel results..Most of the material in the bookhas not previously been published in a single self-contained volume, and can be used for a one- or two-semester graduate topics course. It is complete with helpful exercises and an appendix which describes a

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cept of strongly orthogonal martingales and orthogonal polynomials come into play. Examples include the Gamma process, where again the Laguerre polynomials turn up, the Pascal process, with again the Meixner polynomials, and the Meixner process, with as expected the Meixner-Pollaczek polynomials. Wh

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Masoumeh Azghani,Farokh Marvastiystem characteristics such as system size probabilities, expected system size, etc. The Karlin and McGregor representation [59], [74] of the transition probabilities, which uses a system of orthogonal polynomials satisfying a three-term recurrence relation involving the birth and death rates, is ver

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2296-5009 l appeal to researchers and graduate students in mathematics, applied mathematics and engineering, in particular, engineers working in signal and image processing..978-3-319-34399-0978-3-319-08801-3Series ISSN 2296-5009 Series E-ISSN 2296-5017

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Carlo Bardaro,Ilaria Mantellini,Rudolf Stens,J?rg Vautz,Gianluca Vinti

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Sparse Signal Processing,n extension of random sampling. In this chapter, an overview of compressed sensing, together with a summary of its popular recovery techniques, is presented. Moreover, as a well-known example of structured sparsity, the block sparse recovery problem is investigated and the related recovery approache

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Signal Sampling and Testing Under Noise,p, we introduce signal detection methods relying on nonparametric kernel-type sampling reconstruction algorithms properly adjusted for noisy data. For the off-line testing problem we examine the .. - distance detection statistics measuring the discrepancy between a parametric and a nonparametric est