» » Introduction to Probability

Introduction to Probability download epub

by Dimitri P. Bertsekas,John N. Tsitsiklis


Epub Book: 1553 kb. | Fb2 Book: 1637 kb.

Introduction to Probability. Dimitri P. Bertsekas and John N. Tsitsiklis. Our main objective in this book is to develop the art of describing un-certainty in terms of probabilistic models, as well as the skill of probabilistic reasoning.

Introduction to Probability. Professors of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts. The rst step, which is the subject of this chapter, is to describe the generic structure of such models, and their basic properties. The models we consider assign probabilities to collections (sets) of possible outcomes.

by Dimitri P. Bertsekas (Author), John N. Tsitsiklis (Author). ISBN-13: 978-1886529403. 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.

Dimitri P. Tsitsiklis Massachusetts Institute of Technology. Bertsekas, Dimitri . Tsitsiklis, John N. Introduction to Probability Includes bibliographical references and index L Probabilities. 2. Stochastic Processes. B475 2008 51. - 21 Library of Congress Control Number: 2002092 167 ISBN 978-1-886529-23-6. To the memory of Pantelis Bertsekas and Nikos Tsitsiklis. Probability is common sense reduced to calculation Laplace. Bertsekas and John. An intuitive, yet precise introduction to probability theory, stochastic processes. 37 MB·222 Downloads·New! An intuitive, yet precise introduction to probability theory, stochastic processes. 9 MB·13,698 Downloads.

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.

Bertsekas, Dimitri . Introduction to Probability. The book covers the fundamentals of probability theory (probabilistic mod-els, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a rst course on the subject. Includes bibliographical references and index. It also contains, in Chapters 4-6 a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. Bertsekas, John N. An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. 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. Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3). 4 Mb. Category: Математика, Вычислительная математика. 3. 9 Mb. Parallel and Distributed Computation: Numerical Methods (Optimization and Neural Computation). Bertsekas, John Tsitsiklis. 5. 3 Mb. Dimitri Bertsekas And John N Tsitsiklis. Category: Математика, Probability and statistics.

An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. This 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. 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, least squares estimation, the bivariate normal distribution, and 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. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

Comments: (7)

Mr_Mix
I have used this book and the OCW course for some months now for self study. There are certainly several things to like here:

- Consistent and unambiguous notation, few typos, all the mathematics is done very neatly.

- Great selection and sequencing of the subjects.

- Broad coverage, several concepts which are very useful for problem solving but not emphasized by other books enough like conditioning are full developed here.

- Lots and lots of examples (perhaps even too many, as it seems to have come at the expense of finding better explanations for things)

- Huge number of problems of varying difficulty, including most of the classic ones (Two envelopes, Gamblers ruin, St. Petersburg, Monty Hall, etc.)

Where the book falls short is providing a continuous narrative that would spark any kind of interest in the subject. Concepts are often introduced out of the blue sky, a few examples are shown, and the next part of material proceeds. Excellent example is the section on the normal distribution - formula for it is introduced, some example problems are solved, significant time is spent discussing the use of the tabulated distribution (I would prefer a link to Wolfram Alpha, is there anything particularly noble about using a table instead?) and that's about it, there is not enough motivation that would help with recognizing the concepts in real world settings.

Throughout the book you do learn to apply the concepts and theorems, probably enough to solve some textbook problems, but there is too little discussion to develop a deeper understanding. History of the subject is almost completely exempt from the book, so are the so called "philosophical" issues (actually as most will admit of crucial importance in practical applications), and the "applied" examples often feel contrived ("random variable X" is said to be a "signal intensity", but there is not much more to it) or simply uninteresting, while so many great elementary yet non-trivial examples are to be found among real applications (in machine learning, probabilistic algorithms, information theory, coding theory, econometrics, ...). Neither connections to other areas of mathematics get much explored. The exception are the end of chapter exercises, where some more interesting examples and concepts appear.

I am reading in parallel Hamming's "The Art of Probability", a book that strongly emphasizes everything that this book omits, but to be fair is also nowhere near as throughout or well-organized as this Bertsekas/Tsitsiklis book. It's funny however that Hamming's book, from 1994, is way more informed by the invention of the computer than this book from 2008, including discussion of simulation, coding theory, numerical issues etc. Hamming also derives the Normal distribution starting from a real world problem, and not just presents the formula. I do hope the authors will publish a third edition that keeps the many advantages of this book but addresses those deficiencies. Meanwhile, I do recommend this book, but I also recommend supplementing it with another one that motivates the subject better, like the book by Hamming.
IGOT
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.
Dagdage
I used this book, and the MOOC that goes along with it, to help save me from my boring Statistics class. Instead of hundreds of formulas that all look the same this book gives you a few principles of probability and ties everything together nicely. It actually ended up being really useful for a computer science course I took in data mining. If you need to understand probability then this is a fantastic book. It doesn't water down the material and still manages to be understandable. Probability isn't an easy subject but this book makes it manageable.
Danrad
Our students love it. Clear explanations, many nice problems. Good coverage for an introductory course.
Zetadda
This book is generally excellent, with clear explanations and a good balance of rigor and practical application. You won't find proofs of everything, but you will find excellent guidance and intuition through the various topics, especially the fundamentals. The only significant complaint I have is that certain topics are covered too briefly (such as the central limit theorem or stochastic processes) or not at all (e.g. null hypothesis significance testing).

Much of this will be rememedied in a second edition, which will include a welcome added chapter on estimation. We used a preliminary version of the chapter in the probability class for which the book was written, and it's fantastic. It was the most interesting part of the book, and I'm sorry that I didn't wait to buy the book when I could have gotten the final version.

I hope the second edition also fleshes out the chapter on Markov chains, which are presented very tersely, and without the benefit of linear algebra. Studying Markov chains without using linear algebra is like studying differential equations without the Laplace transform; you can do it but it's a heck of a lot harder than it has to be.

In the end, the terse coverage of certain topics is more than made up for by the fine handling of the basics, and I unreservedly recommend the book for anybody studying the topic for the first time, and even as a refresher for those who have.
Cha
I'm taking a P&S course online and--gah, there is no textbook!!! I purchased this and let me tell you, I NEVER would've passed the course without this. Highly, highly recommended!!
Survivors
Introduction to Probability, 2nd Edition
Very good introduction to the probability theory. It is understandable, well written and has a lot of exercises. A perfect match for your first book about probability.
Introduction to Probability download epub
Mathematics
Author: Dimitri P. Bertsekas,John N. Tsitsiklis
ISBN: 188652940X
Category: Science & Math
Subcategory: Mathematics
Language: English
Publisher: Athena Scientific; 1st edition (June 24, 2002)
Pages: 430 pages