Probability and Measure
Edition: 4th 2012
List price: $124.00
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Description: Now re-issued in a new style and format, but with the same reliable content of the Third Edition, Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Retaining the unique approach of the previous editions, this text interweaves material on probability and measure so that probability problems generate an interest in measure theory and measure theory is, then, developed and applied to probability. Probability and Measure provides a thorough coverage of probability, measure, integration, random variables and expected values, convergence of distributions, derivatives and conditional probability, and stochastic processes. In recognition of over thirty years of publication and having impacted tens of thousands of readers, the 2012 Edition features an open and user-friendly design, updated bibliography, improved treatment of Brownian motion, and replacement of queuing theory with ergodic theory. Like its previous editions, this re-issue will be well received by students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory. An extensive supplement of additional notes on the problems and chapter commentaries is available at further cost either independently or as a set with the book.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $124.00
Copyright year: 2012
Publisher: John Wiley & Sons, Limited
Publication date: 3/9/2012
Size: 7.25" wide x 10.25" long x 1.50" tall
|Patrick Billingsley 1925ï¿½2011|
|Borelï¿½s Normal Number Theorem|
|Existence and Extension|
|Measures in Euclidean Space|
|Measurable Functions and Mappings|
|Properties of the Integral|
|The Integral with Respect to Lebesgue Measure|
|Product Measure and Fubiniï¿½s Theorem|
|Random Variables and Expected Values|
|Convergence of Distributions|
|Derivatives and Conditional Probability|