NOOK Study eTextbooks from Barnes & Noble are read with the NOOK Study eReader for your PC and Mac. Learn about NOOK Study
NOOK Study eTextbooks from Barnes & Noble are read with the NOOK Study eReader for your PC and Mac. Learn about NOOK Study
Anonymous
Posted February 20, 2006
This book should definitely not be titled, 'A First Course in Probability.' In 1999, I graduated from the University of Pennsylvania with a bachelor of science degree in electrical engineering. As part of my curriculum, I took a course in probability theory and earned a grade of A+. I am presently at Rutgers working towards my graduate degree in Mathematics, but they would not accept my transfer credit for the probability course, and so I am taking a probability class for the second time. We happen to be using the book by Ross, and I must say, good luck if you are not an expert in the field of probability. Even with my previous mastery of the subject, I can barely follow some of the examples in the book. This has got to be one of the worst introductory texts that I have ever seen.
1 out of 1 people found this review helpful.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted December 23, 2008
I just finished taking a probability course that used Sheldon Ross' book, and I was very appreciative of the detailed examples and explanations. No short changing on ink and space in this textbook - and this was very good! Also, his solid blend of basic example problems leading to the more complex was helpful in building one's foundation as well as leading to the bigger picture. I must say that I also found having most answers in the back of the book, as well as a self-test section for each chapter with detailed answers very useful in practicing, gaining insight and understanding the topic of probability. I have since bought other Sheldon Ross books for my bookshelf.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted January 3, 2007
I used this book in Intro to Probability at South Florida. The book covers all the necessary material, and provides good examples and good problems.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted November 29, 2004
I found this book very useful in my probability course but the title is misleading if you expect it to supply details or spoon fed answers. It is written as an intro to the subject for math students, not for those who need these skills for another field and thus the theory, although relatively simple, is presented in a manner that assumes the reader has prior experience with proofs. The examples are extremely important and instructional so do not overlook them!
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted December 5, 2003
I bought this book as a supplement to the book I was using in my probability class. Although this book did provide SOME, NOT MUCH, but SOME further insight it still lacked proper structure. The book spends much time on theoretical proofs with too little time on applications of the theory. To title the book a 'First Course in Probability' is a fallacy. There are a plethora of assumptions. For example, the book will say...given function xyz, we conclude 123, with out going through the hundreds of steps in between. This is not a book for beginners, nor does it seem like a book for a first course in probability. With the lack for better words, I think the book is grotesquely confusing and absolutely HORRIBLE!! The 1 Star rating doesn't even do the book justice. I would give it 0 Stars, in fact, if there were negative stars, I would provide appropriate deductions.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted May 20, 2003
I had this as a college text book at the senior level in Industrial Engineering. If you have a strong math background it's great. Not recommended for math neophytes. Excellent, if you're ready for it.
Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.Anonymous
Posted December 13, 2002
The book is very difficult to learn from. The text is very dense and the concepts are presented almost exclusively using mathematical proofs with almost no attempt to also provide intuitive explanations. The worst feature is that the proofs and examples, which are the main tools the book uses to teach, skip many steps that the book claims are trivial or obvious. Those steps may be obvious to people who know the subject well, but they weren't obvious to me. In fact, I spent more time tracking down the skipped steps than I did learning the basic material. Another bad feature is that some very important concepts are only discussed in the examples, with no indication in the general text that they're buried close by! In conclusion, read the book as a concise review only after you've already mastered probablility using other sources.
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Posted January 13, 2002
This book is the worst introductory book I have ever read. I am a student studying for Course 1 in the actuary field. This is one of the books on the approved study list of the SOA/CAS and we used it in my Introduction to Probability Class. The majority of people in the class never took a class in probability and this book was over everyone's head. The book gave few examples and the study questions at the end of each chapter were so difficult that the teacher stopped assigning the class homework. The book is just not for beginners. It is for those that have a good background in probability and want to go deeper into the field.
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Posted June 4, 2009
No text was provided for this review.
Anonymous
Posted January 21, 2009
No text was provided for this review.
More About This Textbook
Overview
Editorial Reviews
Booknews
A book/disk introduction to probability for students in mathematics, engineering, and the sciences (including the social sciences and management science) who understand elementary calculus. Presents the mathematics of probability theory as well as many examples of applications, covering combinatorial analysis, axioms of probability theory, conditional probability, random variables, expected value, and major theoretical results of probability. Other subjects include Markov chains, information and coding theory, and simulation. Includes chapter summaries, exercises, and answers. This fifth edition notes optional material, and updates examples to be more accessible to students. Chapter exercises are reorganized to present mechanical problems before theoretical exercises. The disk, new to this edition, allows students to perform calculations and simulations. Annotation c. by Book News, Inc., Portland, Or.Product Details
Related Subjects
Meet the Author
Table of Contents
1. Combinatorial Analysis
2. Axioms of Probability
3. Conditional Probability and Independence
4. Random Variables
5. Continuous Random Variables
6. Jointly Distributed Random Variables
7. Properties of Expectation
8. Limit Theorems
9. Additional Topics in Probability
10. Simulation
Appendix A. Answers to Selected Problems
Appendix B. Solutions to Self-Test Problems and Exercises
Index
Preface
This book is intended as an elementary introduction to the theory of probability for students in mathematics, statistics, engineering, and the sciences (including computer science, the social sciences and management science) who possess the prerequisite knowledge of elementary calculus. It attempts to present not only the mathematics of probability theory, but also, through numerous examples, the many diverse possible applications of this subject.
In Chapter 1 we present the basic principles of combinatorial analysis, which are most useful in computing probabilities.
In Chapter 2 we consider theaxioms of probability theory and show how they can be applied to compute various probabilities of interest.
Chapter 3 deals with the extremely important subjects of conditional probability and independence of events. By a series of examples we illustrate how conditional probabilities come into play not only when some partial information is available, but also as a tool to enable us to compute probabilities more easily, even when no partial information is present. This extremely important technique of obtaining probabilities by "conditioning" reappears in Chapter 7, where we use it to obtain expectations.
In Chapters 4, 5, and 6 we introduce the concept of random variables. Discrete random variables are dealt with in Chapter 4, continuous random variables in Chapter 5, and jointly distributed random variables in Chapter 6. The important concepts of the expected value and the variance of a random variable are introduced in Chapters 4 and 5: These quantities are then determined for many of the common types of random variables.
Additional properties of the expected value are considered in Chapter 7. Many examples illustrating the usefulness of the result that the expected value of a sum of random variables is equal to the sum of their expected values are presented. Sections on conditional expectation, including its use in prediction, and moment generating functions are contained in this chapter. In addition, the final section introduces the multi-variate normal distribution and presents a simple proof concerning the joint distribution of the sample mean and sample variance of a sample from a normal distribution.
In Chapter 8 we present the major theoretical results of probability theory. In particular, we prove the strong law of large numbers and the central limit theorem. Our proof of the strong law is a relatively simple one which assumes that the random variables have a finite fourth moment, and our proof of the central limit theorem assumes Levy's continuity theorem. Also in this chapter we present such probability inequalities as Markov's inequality, Chebyshev's inequality, and Chernoff bounds. The final section of Chapter 8 gives a bound on the error involved when a probability concerning a sum of independent Bernoulli random variables is approximated by the corresponding probability for a Poisson random variable having the same expected value.
Chapter 9 presents some additional topics, such as Markov chains, the Poisson process, and an introduction to information and coding theory, and Chapter 10 considers simulation.
The sixth edition continues the evolution and fine tuning of the text. There are many new exercises and examples. Among the latter are examples on utility (Example 4c of Chapter 4), on normal approximations (Example 4i of Chapter 5), on applying the lognormal distribution to finance (Example 3d of Chapter 6), and on coupon collecting with general collection probabilities (Example 2v of Chapter 7). There are also new optional subsections in Chapter 7 dealing with the probabilistic method (Subsection 7.2.1), and with the maximum-minimums identity (Subsection 7.2.2).
As in the previous edition, three sets of exercises are given at the end of each chapter. They are designated as Problems, Theoretical Exercises, and Self-Test Problems and Exercises. This last set of exercises, for which complete solutions appear in Appendix B, is designed to help students test their comprehension and study for exams.
Using the website students will be able to perform calculations and simulations quickly and easily in six key areas: