I love this book. The explanation is very detailed.... Golberg explained the proof of each formula in 'layman's language'...in other words, his explanation is very understandable, and very concise, with no complication.

It is very good if you have encountered all those very mathematical, hard to understand probability books out there...I'm not saying Golberg's is not mathematical...it is, but it is followed by explanations that are not as complicated, so reading the explanations elevates my understanding in probability VASTLY.

An indespensible reference for high performance networking, this book clearly and precisely addresses the complicated and oft-overlooked subject of queueing theory. Not for the weak of math - elementary knowledge of statistics is a must.

We used this as our primary textbook in a graduate psychology / consumer behavior research methods / data analyis class, and I found it to be an invaluable aid in statistical analysis. From simple measures such as z-scores, t-tests & f-tests, and chi-squares, to more in depth methods such as GLM, ANOVA, and regression, this book is clear and concise. While it can be slightly cursory at times as to the theoretical underpinnings underneath the data analysis methods being used, it is a very helpful book in understanding how to actually make the methods work, and more importantly, knowing which method to apply in which situation.

Untill Prof. Lattin releases his statistical analysis book for behavioral students, Ott's book is the #1 stat resource on my bookshelf.

In 1981 I used this book for a graduate seminar that I taught on time series analysis at UC Santa Barbara. The course was successful because I followed the text closely. It presents the material in a rigorous and cogent manner. At the time there were a few competing books but some emphasized time domain methods and others were strictly frequnecy domain approaches. Fuller balances the two very well and his book is better written and organized than most of the competitors at that time (1981).

Today there are a lot more books to choose from. You can check my listmania list on time series books to get an idea. I particularly like Brockwell and Davis' book as a competitor to Fuller for a graduate level time series seminar.

I took this class last summer with this text. Mr. Ross' instruction is so clear and well done, I didn't need help ONCE. Also entertaining, clever narrative etc. I felt this text gave me a good handle on the subject matter. I did so well with the self paced aspect, I finished before half the term was over (with an 'A') and enrolled in another class.

This book provides a very easy to read account of the development of the stochastic integral. While concentrating on integrators which are continuous local martingales, and thus lacking the full generality of treatment to be found in, for example Dellacherie
and Meyer, the basic constructions are all performed in a fashion which is readily extensible to the more general case. From a teaching point of view this is beneficial if the more general case is to be studied subsequently. Although the arguments can be considerably simplified for specific special cases (e.g. integration with respect to Brownian Motion only), it is useful to understand how the construction fits into the more general case, which also makes less of a discontinuity for the reader who is subsequently to study the general discontinuous theory!

The arguments are presented carefuly, for example all of the necesary conditions being checked explicitly in places where important theorems are to be applied, and there are none of the annoying statements which plague books on Stochastic Calculus along the lines "the reader can readily check", or "see problem 21.2.43" in the middle of proofs. Additionally very few lines are "skipped" in the proofs; while this does mean that they are lacking in brevity, it is strongly to be encouraged when a complex subject is presented to the novice. When the concepts are understood sufficiently well the reader can easily compile "brief" proofs on his own (as a form of revision), but working the other way round frequently, in my experience of supervising a similar course, leads to misapprehensions about the conditions for applying essential theorems.

This is one of the most thorough and detailed books that I have ever seen on stochastic (that is to say, probabilistic) processes with applications, even more thorough than most textbooks that I have seen in West Coast universities. He has a chapter on second-order processes, one on spectral analysis of sationary processes, one on Gaussian processes, several chapters on Markov processes including one chapter on semigroup applications to Markov processes. Concerning the latter, the reader should look at some of my reviews of semigroup books, such as One-Parameter Semigroups by Engel et al., Right-Ordered Groups by Medvedev and Kopytov, etc. Semigroups are a branch of algebra which tremendously simplifies and unifies other fields such as stochastic processes, functional analysis, mathematical logic, etc. Readers who are not specialists in mathematics as well as those who are should request that this book be reprinted and purchase it and learn it with the help of a consultant or tutor (even if the latter just translates it into closer to ordinary English) because the ideas are so useful for many outside fields. If enough people request it, perhaps it will be reprinted. It is published by Springer-Verlag, New York and Berlin and Heidelberg, 1992, and is part of the Springe Series in Statistics. Springer-Verlag/Springer are especially outstanding for their ability to translate things into ordinary English better than most companies, their ingenious organizing ability and clarity of presentation and printing, their selection of up to date issues and research topics, and their general Creative Genius.

This is a truly excellent book written by a renowned probabilist. This book is highly pedagogical and rigorous at the same time. Future readers should be warned that in the beginning the book does appear to be a little unfriendly but very soon the reader is going to discover the amazingly coherent presentation of the material. The material this book covers is difficult and nobody should expect to learn it quickly. But this book certainly beats other available books similar in scope by a long shot. This is a book for people who want to learn the subject somewhat seriously. People who want a quick introduction may not find it very palatable.

This book is the best textbook for introduction to probability. Forget Chung, forget Ross, forget Feller. All these books assume that you know probability and that you are either revising or looking for advanced concepts. But the book in question assumes only some knowledge in calculus. The chapters are easy-paced and there are nemerous exercises to help you understand the concepts. Another factor that adds to the utility of the book, is the thoughtful addition of answers to most of the exercises. This makes the book suitable for self-study. I would heartily recommend the book to any person new to probability.

With this excellently written, lively, and sometimes tear-jerking look into the basic principles of statistics, there is not a chance in hell your kids won't grown up to be rocket scientists.