Introduction To Probability

The book is an exceptional resource for students across various fields, particularly those studying Artificial Intelligence (AI). It effectively demystifies the complexities of probability theory, making it accessible and engaging for readers at all levels. Learning how to calculate the probability of an event is just the beginning; the book guides you through fundamental concepts, including random variables, expectation, and various probability distributions, with clarity and precision.What sets this book apart is its blend of rigorous theoretical framework with practical examples and exercises that reinforce the material. Bertsekas has a unique ability to present intricate topics in a straightforward manner, which not only aids in comprehension but also sparks interest in the subject.For students pursuing AI, this book serves as an essential foundation, as probability theory is a cornerstone of many AI algorithms and models. The book's well-structured approach makes it a valuable reference for both coursework and self-study, providing the necessary tools to understand and apply probabilistic methods in AI.Overall, 'Introduction to Probability' is a must-have for anyone serious about mastering the concepts and applications of probability. It’s not just a textbook; it’s a comprehensive learning tool that prepares you to tackle real-world problems with confidence, especially in the field of AI.

This book did really save me lots of trouble during my Master's in CS. I've come from a background where we studied calculus and linear algebra but no probability and had to start with courses that assumed a first course in probability and statistics. These types of mathematics books are not usually my favorite as I prefer to go for more rigorous treatments but since I was pressed for time I decided to go with this one instead and less than two months later when I'd barely reached the half-way point I felt like everything in my CS courses are beginning to make sense to me. It absolutely did magic if I must say. I certainly recommend this to anyone who wants a very standard and intuitive treatment of probably and statistics. Although I have to add that this is a book that is more appropriate for students of engineering and hard sciences, students of mathematics would probably need a book where these topics are treated from a measure-theoretic point of view.

If you want to learn probability outside of a physical classroom, this book is an excellent choice. Detailed solutions for all end of chapter problems are available for free from the publisher's website. In addition, this book is used for MIT course 6.041, and MIT offers Open Courseware materials on their website for free. This includes videos of the lectures, as well as more solved problems (beyond those in the book itself) in the form of recitations, problem sets, tutorial problems, and past exams from the MIT class.There is also an edX MOOC that uses this book, with a different set of lectures that are less abbreviated than those for the MIT course.The book itself does a good job of presenting many of the classic problems of probability, including the Monty Hall Problem, the Prisoner's Dilemma, the Two Envelopes Paradox, and the St. Petersburg Paradox. Also, since this is used in the MIT electrical engineering and computer science department, there are problems throughout the book relating to subjects such as reliability and signal degradation.Finally, even though this book covers probability (not probability and statistics), it does cover the normal distribution throughout, as well as the Markov and Chebyshev Inequalities, the Central Limit Theorem, and the Law of Large Numbers. It also contains two chapters added in the 2nd edition covering statistical inference (Classical and Bayesian).

Most importantly, you need to be very smart (IQ 130+, MIT straight A students) if you are about to self-teach probability using this book as your first. Examples are convoluted and difficult, need good combinatorics foundations to make full use of the text.I have read quite a few books on the subject, this one is obviously not the easiest to follow, but very well organized.

I bought this book some months ago and I've just noticed that has a missing part. The problem 42 of the first chapter is partially missing (there's just part of the solution and nothing else). I don't know if it's a problem of the book I have, or if it's a problem of the 9th printing of the book in general. Does anyone have the same problem?

Nice book if you need to study Probability on your own.

Many say this is the best single text on introductory probability, and they have a strong case to say so. It is likely the most user-friendly text available on the subject. The material is explained in a conversational fashion, striking an excellent balance between intuition and mathematics - skewed more toward intuition, which is appropriate for an introductory treatment.You can click to read the table of contents for yourself, but I will single out chapters 6 and 7 as, at least my own favorites. In Chapter 6, the side by side treatment of the Bernoulli and Poisson processes is unique, which is surprising as this text makes this look like obviously the best way to treat them. Chapter 7 on Markov chains is more conventional, but still very good, and includes some material on continuous parameter chains - not always covered at this level.As a plus, Tsitsiklis has corresponding lecture videos online, both from MIT and on Coursera.I would supplement this text with Blitzstein's "Introduction to Probability", which treats the material with a very different slant, at perhaps a slightly deeper level in some cases, while still being introductory.

Book is useable, but has been through several students. Notes and highlights on nearly every page. I hope that I don't get blamed/charged for this when I returned it.

the seller is selling original book,quality is super i am satisfied.

Il testo affianca le lezioni on line di probabilità del MIT ed è a mio parere il non plus ultra per chi voglia accostarsi con impegno e serietà a questo meraviglioso argomento

Buen libro para empezar.

Best book on probability I've ever read. Very clear, excellent choice of topics, excellent balance between text and examples. Also used as the textbook in the graduate probability course at MIT taught by prof. Tsitsiklis (co-author) - fabulous course, by the way - , also available online at EdX.One suggestion for improvement: I always try to solve the examples before reading their solution. Yet several examples do not state the problem before solving it :-)

Math manuals about probabilities. Plenty of exercises, just a perfect self-learning book.Don't buy it if you want to read casually, this is a book you'll have lean upon on your desk for a few hours.