| 书目名称 | Measure Theory and Probability Theory |
| 编辑 | Krishna B. Athreya,Soumendra N. Lahiri |
| 视频video | |
| 概述 | Presents the main concepts and results in measure theory and probability theory in a simple and easy-to-understand way.Provides heuristic explanations behind the theory to help students see the big pi |
| 丛书名称 | Springer Texts in Statistics |
| 图书封面 |  |
| 描述 | This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the?rstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a ?rst course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p di?erentation theorems of Lebesgue, L -spaces on R, and do general m- sure at the end of the course with one main application to the construction of product measures. This approach does have the pedagogic advantage of seeing one concrete case ?rst before going to the general one. But this also has the disadvantage in making many students’ perspective on m- sure theory somewhat narrow. It leads them to think only in terms of the Lebesgue measure on the real line and to believe that measure theory is intimately tied to the topology of the real line. As students of statistics, probability, physics, engineering, economics, and biology know very well, there are mass distributions that are typically nonuniform, and hence it is useful to gain a general perspective. This book attempts to provide that general perspective right from the beginning. The opening c |
| 出版日期 | Textbook 2006 |
| 关键词 | Markov Chain; Markov Chains; Measure; Probability distribution; Probability space; Probability theory; Sto |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-35434-7 |
| isbn_softcover | 978-1-4419-2191-8 |
| isbn_ebook | 978-0-387-35434-7Series ISSN 1431-875X Series E-ISSN 2197-4136 |
| issn_series | 1431-875X |
| copyright | Springer-Verlag New York 2006 |
发表于 2025-3-21 19:08:10
