First Look At Rigorous Probability Theory, A

by Jeffrey S. Rosenthal

“This is a fine textbook on probability theory based on measure theory. The parts of measure theory that are needed are developed within the book and a teacher of measure theory could find them quite useful. The construction of the Lebesgue measure (extension theorem) is unusual and interesting.” Mathematical Reviews “This short, lucid and excellent textbook should be a required course for all graduate students of mathematics and statistics as well as for interested graduate / PhD students in engineering, computer sciences, economics, and management who lack exposure to measure theoretic applications in probability theory.” Professor B K Sahu Indian Institute of Technology, India Solutions Manual for Free Download 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.

Genres: Mathematics, Textbooks

236 pages, Paperback

First published November 1, 2006

Reviews

[Mike · Rating 4 out of 5]

Everyone should read this book when taking an advanced probability course. One of the best introductions to the subject.

[Fadi · Rating 5 out of 5]

A neat, concise and comprehensive introduction to the subject.
The text strikes an appropriate balance, rigorously developing probability theory in the language of measure theory while avoiding unnecessary details.