书目名称 | Random and Quasi-Random Point Sets |
编辑 | Peter Hellekalek,Gerhard Larcher |
视频video | |
丛书名称 | Lecture Notes in Statistics |
图书封面 |  |
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 |
关键词 | Generator; Kernel; LDA; Probability theory; statistics |
版次 | 1 |
doi | https://doi.org/10.1007/978-1-4612-1702-2 |
isbn_softcover | 978-0-387-98554-1 |
isbn_ebook | 978-1-4612-1702-2Series ISSN 0930-0325 Series E-ISSN 2197-7186 |
issn_series | 0930-0325 |
copyright | Springer Science+Business Media New York 1998 |