This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the firstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a first course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p differentation 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 first 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 chapter gives an informal introduction to measure and integration theory. It shows that the notions of σ-algebra of sets and countable additivity of a set function are dictated by certain very na- ral approximation procedures from practical applications and that they are not just some abstract ideas.
1100018647
Measure Theory and Probability Theory
This book arose out of two graduate courses that the authors have taught duringthepastseveralyears;the firstonebeingonmeasuretheoryfollowed by the second one on advanced probability theory. The traditional approach to a first course in measure theory, such as in Royden (1988), is to teach the Lebesgue measure on the real line, then the p differentation 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 first 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 chapter gives an informal introduction to measure and integration theory. It shows that the notions of σ-algebra of sets and countable additivity of a set function are dictated by certain very na- ral approximation procedures from practical applications and that they are not just some abstract ideas.
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Measure Theory and Probability Theory
619
Measure Theory and Probability Theory
619Paperback(Softcover reprint of hardcover 1st ed. 2006)
$99.99
99.99
In Stock
Product Details
| ISBN-13: | 9781441921918 |
|---|---|
| Publisher: | Springer New York |
| Publication date: | 11/23/2010 |
| Series: | Springer Texts in Statistics |
| Edition description: | Softcover reprint of hardcover 1st ed. 2006 |
| Pages: | 619 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.05(d) |
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