Item description for First Look at Rigorous Probability Theory by Jeffrey S. Rosenthal, Harry Bliss, Sami Makkonen, Andrew Frisardi, Jed Hallam & Ian Penney...
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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Studio: World Scientific Publishing Company
Est. Packaging Dimensions: Length: 1" Width: 6.25" Height: 9.25" Weight: 1.2 lbs.
Release Date Nov 14, 2006
Publisher World Scientific Publishing Company
ISBN 9812703705 ISBN13 9789812703705
Availability 63 units. Availability accurate as of Mar 25, 2017 09:55.
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More About Jeffrey S. Rosenthal, Harry Bliss, Sami Makkonen, Andrew Frisardi, Jed Hallam & Ian Penney
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.
Jeffrey S. Rosenthal has an academic affiliation as follows - University of Toronto.
Reviews - What do customers think about First Look at Rigorous Probability Theory?
There are better, but it deserves the 5 stars. Nov 29, 2007
This book I would recommend to prepare you to study Billingslley's Probability and Measure theory. I personally prefer Jacot and Proter's Probability Essentials, for this preparation; however, I can't underestimate this book's quality, hence the 5 stars.
A gem Jul 17, 2007
This is my bedside book at the present time. It's compact, written with immense respect for the reader and even covers some financial applications. It's recalling the measure theory I learned as an undergraduate with the right style. So much better than some of the "Probabilty from dummies" I have put away. When I finish the book I hope to move on to some of the heavier books with a clear idea of where I am going.
Best Probability book ever! Jul 10, 2006
As a graduate student in mathematics I appreciate the rigorous and no nonsense treatment of the subject. I'm am using this text to study for my Ph.D. qualifying exam in statistics. It's explaining statistics in a language I understand.
Excellent primer to use as supplement or for review Mar 15, 2002
This is a marvelous primer on measure-theoretic probability. I came across it a couple of years after taking a course based on Chung's famous text ("A Course in Prob. Theory") and found it to be an excellent book for review and remediation--that is, it helped me get a better overview of the material I had already learned and it helped me learn topics such as, say, uniform integrability, that didn't sink in too well the first time around.
According to the preface, the author prepared most of the book as supplemental class notes for the benefit of his students in a course whose main text was, if I recall correctly, Billingsley's excellent "Probability and Measure". The students were so enthusiastic about the usefulness of Professor Rosenthal's supplemental info that they insisted he publish it, despite his objection that the book wasn't original enough to warrant entry into an already crowded field. Well, the students made the right call: Rosenthal's clear and concise text will, I think, help almost any student learn measure-theoretic probability more efficiently. I'd also recommend it to folks who need a concise review of measure-theoretic probability.