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Introduction To Probability
An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. The 2nd edition is a substantial revision of the 1st edition, involving a reorganization of old material and the addition of new material. The length of the book has increased by about 25 percent. The main new feature of the 2nd edition is thorough introduction to Bayesian and classical statistics.
The book is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject, as well as the fundamental concepts and methods of statistical inference, both Bayesian and classical. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes.
The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Written by two professors of the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and members of the prestigious US National Academy of Engineering, the book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.
From a Review of the 1st Edition:
...it trains the intuition to acquire probabilistic feeling. This book explains every single concept it enunciates. This is its main strength, deep explanation, and not just examples that happen to explain. Bertsekas and Tsitsiklis leave nothing to chance. The probability to misinterpret a concept or not understand it is just... zero. Numerous examples, figures, and end-of-chapter problems strengthen the understanding. Also of invaluable help is the book's web site, where solutions to the problems can be found-as well as much more information pertaining to probability, and also more problem sets. --Vladimir Botchev, Analog Dialogue
Several other reviews can be found in the listing of the first edition of this book. Contents, preface, and more info at publisher's website (Athena Scientific, athenasc com)
The book is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject, as well as the fundamental concepts and methods of statistical inference, both Bayesian and classical. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes.
The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Written by two professors of the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and members of the prestigious US National Academy of Engineering, the book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.
From a Review of the 1st Edition:
...it trains the intuition to acquire probabilistic feeling. This book explains every single concept it enunciates. This is its main strength, deep explanation, and not just examples that happen to explain. Bertsekas and Tsitsiklis leave nothing to chance. The probability to misinterpret a concept or not understand it is just... zero. Numerous examples, figures, and end-of-chapter problems strengthen the understanding. Also of invaluable help is the book's web site, where solutions to the problems can be found-as well as much more information pertaining to probability, and also more problem sets. --Vladimir Botchev, Analog Dialogue
Several other reviews can be found in the listing of the first edition of this book. Contents, preface, and more info at publisher's website (Athena Scientific, athenasc com)
528 pages, Hardcover
First published June 24, 2002
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Displaying 1 - 11 of 11 reviews
August 19, 2016
Well done textbook introducing all the main topics in probability, as well as Markov Chains, Bayesian Statistical Inference, and Classical Statistical Inference.
I would also recommend the free MIT course at edX, Introduction to Probability - The Science of Uncertainty, taught by the author of this book, John Tsitsiklis.
I would also recommend the free MIT course at edX, Introduction to Probability - The Science of Uncertainty, taught by the author of this book, John Tsitsiklis.
May 4, 2020
Back in University I remember how hard was for me to get Probability, still was a fascinating topic but by that time I couldn't grasp the concepts properly, earlier this year I got back to it by taking a class from MIT open courseware and it was fantastic, I used this textbook as compliment, did the exercises and went through the majority of the explanations and tbh it was really good experience going from almost nothing to having a grasp of probability theory and how important is in real applications and life in general. Totally recommend this book.
December 24, 2018
I took MITx-6.431x on edX, that's what brought this book to my hands.
The chapters are full of examples, and the problems are challenging and fun. There are hundreds of them by the way. Of course, challenging can also be "unfun" and I felt sometimes out of my flow zone, but I believe it's worth the efforts put into it.
The chapters are full of examples, and the problems are challenging and fun. There are hundreds of them by the way. Of course, challenging can also be "unfun" and I felt sometimes out of my flow zone, but I believe it's worth the efforts put into it.
August 4, 2021
The statistics part seems a bit rushed, but the probability chapters are great, very clear, notation is clear and consistent throughout the book, problems are quite challenging. At times it seems that authors did not assume any prior mathematical knowledge of the reader, which makes certain parts look odd, like looking at Markov processes without using any matrix algebra. Otherwise this is a great introductory probability text for self-learners.
August 11, 2021
Excluding Bayesian part :(
June 28, 2024
This book is absolutly 5 five star for me. I took the similar class in university more than 10 years ago and had never understood what probability is (maybe some little classic probablity). These days my work is all about randomness and push me to study the ideas in probability again. It's very tough for a guy who had gradutaed for more than 10 years, but the book offers a fast pace course (MIT 6.041) which helps me grasp the key ideas and read the whole book quickly.
December 17, 2020
Textbook used as part of the MITx course "Probability Theory: The Science of Uncertainty", written by the same course instructors. Very good resource, I think people interested in the subject can even pick up the book without the guidance of the course and still learn a lot from it.
MITx course: https://www.edx.org/course/probabilit...
MITx course: https://www.edx.org/course/probabilit...
July 18, 2018
A comprehensive and well-written introduction to probability theory and statistics. I particularly appreciated the careful attention to notation and the formal yet straightforward. Some of the exercises can be hard so if you don't have experience, some patience is needed when going through them.
May 21, 2020
A solid book on applied probability, random processes, and statistical inference with intuitive explanations and challenging problem sets. Would highly advise going over MIT OCW course 6.041SC in parallel while reading the book.
December 6, 2020
Great clarity and not-too-dry illustration. I wished I had this book back in college...
December 14, 2008
This is an excelent introduction to calculus based probability. It is easily accessible to people coming from any dicipline.
I read it as part of my graduate studies at GMU (ECE 528).
I read it as part of my graduate studies at GMU (ECE 528).
Displaying 1 - 11 of 11 reviews