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Titlebook: Random and Quasi-Random Point Sets; Peter Hellekalek,Gerhard Larcher Book 1998 Springer Science+Business Media New York 1998 Generator.Ker

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發(fā)表于 2025-3-21 16:30:32 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Random and Quasi-Random Point Sets
編輯Peter Hellekalek,Gerhard Larcher
視頻videohttp://file.papertrans.cn/822/821108/821108.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Random and Quasi-Random Point Sets;  Peter Hellekalek,Gerhard Larcher Book 1998 Springer Science+Business Media New York 1998 Generator.Ker
描述This volume is a collection of survey papers on recent developments in the fields of quasi-Monte Carlo methods and uniform random number generation. We will cover a broad spectrum of questions, from advanced metric number theory to pricing financial derivatives. The Monte Carlo method is one of the most important tools of system modeling. Deterministic algorithms, so-called uniform random number gen- erators, are used to produce the input for the model systems on computers. Such generators are assessed by theoretical ("a priori") and by empirical tests. In the a priori analysis, we study figures of merit that measure the uniformity of certain high-dimensional "random" point sets. The degree of uniformity is strongly related to the degree of correlations within the random numbers. The quasi-Monte Carlo approach aims at improving the rate of conver- gence in the Monte Carlo method by number-theoretic techniques. It yields deterministic bounds for the approximation error. The main mathematical tool here are so-called low-discrepancy sequences. These "quasi-random" points are produced by deterministic algorithms and should be as "super"- uniformly distributed as possible. Hence, both i
出版日期Book 1998
關(guān)鍵詞Generator; Kernel; LDA; Probability theory; statistics
版次1
doihttps://doi.org/10.1007/978-1-4612-1702-2
isbn_softcover978-0-387-98554-1
isbn_ebook978-1-4612-1702-2Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media New York 1998
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Lecture Notes in Statisticshttp://image.papertrans.cn/r/image/821108.jpg
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https://doi.org/10.1007/978-1-4612-1702-2Generator; Kernel; LDA; Probability theory; statistics
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978-0-387-98554-1Springer Science+Business Media New York 1998
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Book 1998e will cover a broad spectrum of questions, from advanced metric number theory to pricing financial derivatives. The Monte Carlo method is one of the most important tools of system modeling. Deterministic algorithms, so-called uniform random number gen- erators, are used to produce the input for the
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From Probabilistic Diophantine Approximation to Quadratic Fields, natural geometric classes like aligned and tilted rectangles, squares, boxes, circles, balls, convex sets, etc. (or residue classes, etc.). It has important applications in number theory, combinatorics and computational geometry.
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On the Assessment of Random and Quasi-Random Point Sets,e influence on our numerical results. In this contribution, we will discuss the main concepts to assess equidistribution. In particular, we will present a new interpretation of the well-known spectral test.
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