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Synopsis
“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.
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About the Author
JEFFREY S. ROSENTHAL is a professor in the Department of Statistics at the University of Toronto. At 24, he received his Ph.D. in mathematics from Harvard. He has written two textbooks on probability theory and is also an amateur musician and computer-game programmer. He lives in Toronto. Visit him online at www.struckbylightning.ca.
Review
"This book is certainly well placed to establish itself as a core reading in measure-theoretic probability ... Ýit is¨ delightful reading and a worthwhile addition to the existing literature."
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First Look at Rigorous Probabi
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