| 书目名称 | Monte Carlo Strategies in Scientific Computing |
| 编辑 | Jun S. Liu |
| 视频video | |
| 概述 | The author is a leading researcher in a very active area of research.Emphasis is on making these methods accessible to scientists who want to apply them.Includes examples from artificial intelligence, |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .This paperback edition is a reprint of the 2001 Springer edition....This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as the textbook for a graduate-level course on Monte Carlo methods. Many problems discussed in the alter chapters can be potential thesis topics for masters’ or Ph.D. students in statistics or computer science departments....Jun Liu is Professor of Statistics at Harvard University, with a courtesy Professor appointment at Harvard Biostatistics Department. Professor Liu was the recipient of the 2002 COPSS Presidents‘ Award, the most prestigious one for statisticians and given annually by five leading statistical associations to one individual under age 40. He was selected as a Terman Fellow by Stanford University in 1 |
| 出版日期 | Book 2004 |
| 关键词 | Excel; Markov Chains; Markov chain; Monte Carlo Method; Potential; Probability theory; Random variable; Sci |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-76371-2 |
| isbn_softcover | 978-0-387-76369-9 |
| isbn_ebook | 978-0-387-76371-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2004 |
发表于 2025-3-21 18:35:22
