The content of this book is probably one of the most exhaustive that covers both introductory materials and rather wide-ranging and advanced applications of probability in various engineering disciplines. It is useful for engineering students who wish to see the applicability of probability to real world problems. The main strength lies in Chapter 15, which presents examples of some engineering systems that, involved sophisticated probabilistic analysis. The remaining chapters deal with more advanced topics without the painstaking rigor of the theory involved. They serve as a quick reference to most probabilistic problems encountered in engineering applications and are best suited to practitioners who merely want to know how to use results. These will also fit in well as notes for training course as more detailed insights and discussions would have been supplemented and the practical limitations of the results would have been explained during the course.

Dr. Suhir's book provides a rare and excellent combination of an in-depth and comprehensive introduction to the subject of the applied probability for engineers and scientists, and a useful guide for solving versatile practical problems associated with uncertainty in the analysis and design of various engineering and physical systems. Whatever your field of engineering or applied science, you will find the book both enlightening and helpful.

Dr.Suhir has recently published a very well reviewed book entitled "Applied Probability for Engineers and Scientists", McGraw-Hill, 1997, that has an excellent treatment of applications of the theory to practical problems. In addition to its scholarship, what struck me about the book was how well it was written.

The most important quality to note about this book is that it is *indeed* a graduate textbook. Thus, anyone who is considering reading this book and who has not yet studied ideas such as random processes (including Markov chains and martingales), the Central Limit Theorem, etc.. should pass on this book and look into one or more undergraduate texts (Ross's "Probability Models" would make a good start). Another question the potential buyer should ask is, "Do I want to apply probability, or do I want to study probability for the sake of probability?". If it is the former, then there are many more suitable books whose union covers all the topics in this book, and does so in a much more clear and inuitive manner (For example, "Ergodic Theory" by Halmos, "Probability Models" and "Random Processes" by Ross, and "Mathematical Statistics Chapters 1-8" by Roussas is one example of such a covering). On the other hand, if the answer is the latter, then stop here and begin reading! In other words, the 5 stars for this review is relative to anticipating that the reader is a grad. student who wants to do research in probability and/or statistics. Conditioning on these assumptions, the clarity and coverage found in this text cannot be matched. The measure-theoretic approach may take some time to get used to, but in the end the reader will be thankful for this, as many of the proofs fall out quite easily.

When I started to develop an interest in statistics and probability I took the challenge of finding a concise probability book that balanced both the formal aspects of the theory as the practical approach. After many books, I decided to take a look a springer-verlag's related texts. I was surprised when I found this book since, even though its name (Graduate Texts in Mathematics, me being an undergrad), it is a down-to-earth book that takes by the hand through probability theory. Among the many books I read, (in some cases just tried to read), Shiryaev's book was the most organized and efficient one: it didn't lose valuable time explaining obvious concepts nor did it overshadow the underlying ideas with complex mathematical formalisms: it was simple and comprehensive. I think this book is highly recommendable to any science student familiarized with the most essential mathematical concepts, who wishes to have in the bookshelf and near of hand a complete probability course (in reality, the only knowledge needed is elementary calculus, series, and a set theory and a very basic notion of probability doesn't hurt). This book, in combination with an applied probability book, is more than enough to understand this fascinating and necessary subjet (I also recommend in adition to this text, "Mathematical Statistics" by Terrell, also a Springer-Verlag)

A fantastic book for those who wish to gain a comprehensive exposure to rigourous probability theory. Ideal for self-study. Beautiful presentation, with little typos here and there (previous edition). Terrific example of clarity and precision in exposition. All fundamental results. Theory. If you want to spend the due time, and read line by line this book is invaluable and totally tractable. Prerequisite: An elementary course in Analysis (and may be not even that). Bon-appetit.

I've been reading this book along with Durrett's PROBABILITY: THEORY AND METHODS and Williams' PROBABILITY WITH MARTINGALES. I also have Billingsley's PROBABILITY AND MEASURE. All of these are good books, pitched at roughly the same level. Here are a few more specific reactions:

1) Measure theory background: Resnick & Billingsley assume no background in measure theory and interleave the relevant measure theory with probability. Durrett & Williams have appendices on measure theoretic results which cover more or less the same ground.

2) Mathematical level: Resnick is a easier than the others. He spells out lots of details in the proofs that are either left as exercises or omitted altogether in the other books. I found myself reading a statement in Resnick, asking myself why the statement was true, working out the answer easily--only to find that Resnick provided the details shortly thereafter. Sometimes this is a good thing, sometimes a little tedious.

3) Style: I'd rate Resnick below Williams and Billingsley. Williams has very elegant proofs and covers as much material as Resnick in half as much space. Billingsley is wonderfully eclectic and makes connections to lots of other areas of mathematics. Resnick is easy enough to understand, but is much more workman-like.

I think Resnick fills an important niche in this literature. I think it's a good book for teaching. I also refer to it frequently when I'm confused by something in the other books. It's thorough, relatively easy to understand, and seems to be accurate.

This is indeed the best probability book I have ever read. It is the only non-elementary probability book (to my knowledge) which even non-mathematicians (e.g.: economists, engineers) may find easy to read. It provides a satisfactory treatment of measure-theoretic probability, it covers a good number of topics and it provides nice and eminently readable proofs for every theorem. All mathematics books should be written like this one: there are no oversimplifications, advanced results are presented when needed, everything is carefully proved, but it never lacks of explanations, nor too much knowledge is assumed of the reader (just a bit of calculus and elementary probability). You never have to struggle to understand things, because the author does not save words or formulas, in order to make everything clear. If I could give this book ten stars, I would. It is incomparably easier to read than Williams, Jacod and Protter, Capinski and Kopp, Ash or Billingsley. I reccomend it especially to financial economists willing to study seriously stochastic processes and stochastic differential equations.

Sid Resnick provides a very readable account of probability theory. This book is more like a teacher than just a mathematics book. Resnick gives a lot of motivation for every definition and every step in the proofs. He also discusses applications of the developed theory, and gives a wealth of examples in every chapter.

One notable feature of this book, which I consider unique, is that after every definition basic facts and properties related to the definition are given in an exhaustive way. This is different than the general approach which distributes such facts into proofs, and makes it harder to follow.

This is a 31 page pamphlet reproducing chapter 2 of Tufte's 1997 "Visual Explanation:images and Quantities, Evidence and Narrative". It contains two case studies: doing it right illustrated by John Snow's famous Cholera investigation and doing it wrong showing charts used to determine the ill-fated challenger accident (could almost be renamed as an example of liing with charts what what to suspect).

Production values are unusually high (which we'd expect from Tufte) with heavy paper, well printed, excellent illustration and color pictures. The pages are large 8.5"x11". The only thing I'm concerned about is the durability of the cover pages (paper back).

Two really good eamples, one good/one bad, of the use of charts.
Low price, 5 stars.

In "Visual & Statistical Thinking", Edward Tufte (a professor at Yale University, where he teaches statistical evidence and information design), provides two case study-like topics that explores how graphs and images provide better decision making. This clearly written booklet reiterates his focus on his other books: (1) The task in making decisions based on evidence is understanding how thing work (cause and effect), and (2) making decisions based on evidence requires appropriate display of that evidence. Good charts and images help reveal knowledge relevant to making informed decisions.

This booklet was a required text for a knowledge management course. I recommend this and all his books if you are an information architect, web designer, graphic artist, or anyone who works with providing and displaying data and information to others. Well worth the $$$

Edward Tufte has written 3 big, justifiably famous and well liked books. They're also beautiful and expensive. This is really a booklet, a reprint of a chapter of one of his books, and is a great way to get started on the way he thinks. It explores how graphics were used to track down the source of a cholera epidemic in London -- and how bad chart-making and graphics could have led to the wrong conclusion. The second example in this excerpt explores the explosion of the space shuttle Challenger. In this absorbing example, Tufte takes the 13 pages of (badly organized) data that the engineers debated on the night before the space shuttle was scheduled to blast off. Tufte first tears apart the charts, and demonstrates why even though the engineers reached the right conclusion (don't launch), why the data was presented so badly that NASA overruled them (resulting in the Challenger explosion). Then, Tufte rearranges the same data into a couple of clear graphic displays that demonstrate they clearly had enough data to demonstrate that the launch of the Challenger was clearly occurring at grave risk. A great example of clear thinking at work.

OK, so maybe great graphics won't save the world. But this is a good, well priced introduction into Tufte's line of thinking. If you think you might like his stuff, buy this; get hooked; buy the big books.

As a certified Black Belt, I have to perform statistical tests on diverse processes for both manufacturing and service industries.

I have found this book to be the best I know of to be my "desktop guide" when I have any doubt on the method or applicability of a test or tool.

I engourage any and all readers who are looking for a book to help them do Process Improvement or Six Sigma Process Improvement to get this book.

It is well worth the price.

This book is really great, I reccommend it!

Why don't publishers bring book prices down? They would then sell more books (so they would still make money), as well as improving the accessibility of the author's work.

Twenty years ago, in the process of moving, my copy of this book fell and was buried in the West Virginia mud. It was retrieved and dried. Although it still retains the marks of its misadventure, it is always the first statistics book on my shelf that I turn to when I encounter a problem that I do not understand. This little book is clearly and succinctly written with the lay-person in mind. I have read it from cover to cover. Of what other mathematics text can this be said? I especially like the chapter on the Poisson distribution. My poor, sad, mishandled copy is beaten and worn. I am distressed that it is out of print. It belongs on the bookshelf of every scientist with the remotest intention to employ statistics.

A classic for those who know just enough to get themselves in trouble. Have been using it, (or trying to get it back from someone who "borrowed" it) for many years.

I had never gone further than standard deviations and lines of regression at school, and dearly wanted to learn more. This book proved to be the ideal choice. Each subject was introduced in a simple and intuitively appealing manner, leading logically on from one to the next. It is a real pleasure to read, for layman and technically-minded statistics-novice alike.

Despite its age, its examples remain forever pertinent (after all, a standard error on the standard deviation of a sample of measurements on boys' heights, weights and examination marks remains the same in the 1990s).

Got it as a reference book to help me understand statistical tests. Havent used it much in my daily work, but its very comforting to know that its there. Quite readable, and extremely bulky to handle. Guess this will go on to be a classic in the field ..sad reality is that most of the methods discussed are already in programs like Minitab & S-Plus, so you can do most of the analysis without slogging through the theory.

This is an excellent, exceptionally comprehensive, and well-structured reference on a wide range of inferential tests and measures of association, which is ideal for the applied scientist. After a few useful introductory chapters that provide definitions and outline the main concepts involved in inferential statistics, the various tests are covered chapter by chapter. Each chapter contains sections describing:
1. the hypotheses evaluated and relevant background information;
2. examples of the kind of problems that can be addressed using the test
3. the null and alternative hypotheses;
4. computation (including meticulously worked out examples so the reader can follow the precise workings);
5. an extensive set of notes describing the interpretation of the results, the assumptions of the test, the robustness of the test to violations of those assumptions, and comparisons with alternative tests;
6. a set of references.
A set of decision tables are provided to assist the user in selecting the appropriate test, and there are additional extensive discussions in the various chapters to assist further if required. The book also includes an extensive list of look-up tables for significance testing. An additional valuable feature of the book is that in the chapter on the nonparametric test for a difference in medians of two populations (Mann-Whitney U-test) there is included discussions about permutation and randomization tests.

The target audience of the book is the practioner rather than the theoretician. The book aims to assist in the selection of an appropriate test and the interpretation of the test results rather than on a theoretical discussion of the test. The text is exceptionally clearly written, and is highly accessible to non-experts in statistics. There is a minimum of equations, which are supplied only where necessary. While it is not that hard to find a few editorial omissions, the book does seem to have been edited carefully, and I have as yet stumbled across only trivial errors. If I were forced to find any criticism, about the only thing I could say is that at the top of the page the chapter headings are listed purely in terms of test number without listing the test name, which sometimes makes it a little harder to find the test of interest.

I make absolutely no hesitation in recommending this book to anyone who makes use of inferential statistics.

Excellent book for handling all common and uncommon stats tests. Plenty of examples for application, an overview for each. This book is good for those that have had stats and need a reference guide on them later. I would not recommend this for anyone that has not actaully formally studied higher end stats.

It is a very practical book. Helped me to become a much better trader. This book has a very unique perspective and interesting ideas that can be very useful

I've been trading for 15 years and I read as many trading books as I can, and I found High Probability Trading to be one of the best yet. There is no bull or get rich quick trading system in this book, instead the Author lays out some simple but often over looked principles of how to become a better trade. For example he stresses that trading with trend has always been the way to go and one should not get to fancy by trading the counterwaves. Its a simple prinicple but one that I can forget about at times. This book was a great refresher and taught me a few things about my trading, i highly recommend it to any trader.

Glanced the book at a local bookstore, couldn't put it down. Plan to order the book through Amazon.com Thanks Mr. Link. One gem I learned from the book for day-traders/swing traders as to how to use ATR. Planning to check ATR before I chase any stocks. Well, I shouldn't be chasing it in the first place. That's the one single thing I'm unable to overcome consistently yet. This book has lot of gems in it for me to help to manage my weakness. This book will join the classic list of stock market books in my humble opinion. This book joined my personal top ten list already. Great one.

This is the best book available on interest rate models. Very detailed. Much more focused and readable than Rebonato's book. More pragmatic and explicit than Musiela and Rutkowski. Not as theoretical as Hunt and Kennedy. James and Webber also looks very good, but I'm not that familiar with it. All other books have only bits and pieces on interest rates.

With all the due respect to the other authors I would say that if one is interested in a good theoretical book whihc is also good on the implementation side then the book of Brigo and Mercurion is definetly the best book I have ever read on the subject.

Anyone interested in implementing the LMM/BGM/MSS model in practice is well advised to read it.

I would just say that this is certainly a must have in the field.

In the late nineties I went through Brigo's innovative work on stochastic nonlinear filtering with differential geometry techniques. I was favorably impressed by results and style, particularly in his dissertation and in his 'geometry in present day science' very readable overview. Interesting results are found and nicely told with accurate - but not pointlessly complicated - advanced mathematics for the problems at hand, I reasoned.

I've followed a similar path from control to finance, and having worked with interest rate models, I couldn't help but order this Brigo-Mercurio book. I had high expectations 'cause these two guys are working in a bank on the real thing.

Sure enough I'm not disappointed.

1-factor models are handled with great care, a ton of formulas and recipes are given. I've never seen this kind of analysis of pricing with Gaussian 1-f models. The new upgrade of the CIR model is interesting and accurate. "CIR++" is now my favorite 1-f model. I like the treatment of lognormal 1-f models and the explanation of Monte Carlo and trees -- the flow-chart for Bermudan swaptions is crystal clear! Plots of market implied structures and volatility calibration are useful additions.

The chapter on 2-f extensions has one of the best discussions on volatility, and two tons of useful formulas/recipes. Two dimensional trees!

The HJM chapter size is OK. I agree - the useful models embedded in HJM are short rate models and market models.

Market models - these three chapters alone are worth the book. You'll find yourself nodding as you read the guided tour. They make it look easy all the time. The exposition is focused, clear, intuitive, detailed. There's also new stuff, just check the calibration discussion! Smile modeling begins with a brilliant tour and ends with Brigo-Mercurio's new approach - the mixing dynamics - deserving a whole chapter if expanded.

The detailed explanation on products is a much welcome original addition. Cross currency derivatives!

Quotes - as in Brigo's old work - are a pleasant diversion while reading. The 500 and more pages are a treat given the competitive price.

Still there's room for improvements - more "CIR2++"! Something on 3-f models. Historical estimation of the correlation matrix and low-rank optimized approximations. Expand smile modeling! More hedging. Something on structured products. Cross currency libor model. chapter 9 - other interest rate models - sounds out of place and can be suppressed for other things.

This book rings true and has useful teachings for students, academics and practitioners. Although it requires some background in stochastic calculus, it's hard to beat on the pricing front. Kudos to Brigo and Mercurio! It only harms there aren't enough books like this.