
First Look At Rigorous Probability Theory, A (2nd Edition)
Jeffrey S. Rosenthal(Author)
World Scientific Publishing Co Pte Ltd
2nd Edition
Published on 15. November 2006
Book
Paperback/Softback
236 pages
978-981-270-371-2 (ISBN)
Description
This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results. Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. In this new edition, many exercises and small additional topics have been added and existing ones expanded. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.
More details
Edition
2nd Revised edition
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 13 mm
Weight
349 gr
ISBN-13
978-981-270-371-2 (9789812703712)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition

Jeffrey S. Rosenthal
First Look At Rigorous Probability Theory, A
Book
04/2000
World Scientific Publishing Co Pte Ltd
€21.49
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Content
The Need for Measure Theory; Probability Triples; Further Probabilistic Foundations; Expected Values; Inequalities and Convergence; Distributions of Random Variables; Stochastic Processes and Gambling Games; Discrete Markov Chains; More Probability Theorems; Weak Convergence; Characteristic Functions; Decomposition of Probability Laws; Conditional Probability and Expectation; Martingales; General Stochastic Processes.