Product Description
This brief version of the authors' classic text retains the traditional outline for the coverage of descriptive and inferential statistics. The user-friendly presentation includes features such as Key Concepts and Formulas, and helps students grasp the material while not sacrificing the statistical integrity of the subject. MINITAB™ (Versions 12 and 13) is used exclusively as the computer package for statistical analysis in this text. ... Read more

Customer Reviews (1)

Text book
FAST shipping, book is in Excellent condition. GREAT prices too. I would definately buy from this seller again! ... Read more

Product Description
Many of the problems that engineers face involve randomly varying phenomena of one sort or another. However, if characterized properly, even such randomness and the resulting uncertainty are subject to rigorous mathematical analysis.

Taking into account the uniquely multidisciplinary demands of 21st-century science and engineering, Random Phenomena: Fundamentals of Probability and Statistics for Engineers provides students with a working knowledge of how to solve engineering problems that involve randomly varying phenomena. Basing his approach on the principle of theoretical foundations before application, Dr. Ogunnaike presents a classroom-tested course of study that explains how to master and use probability and statistics appropriately to deal with uncertainty in standard problems and those that are new and unfamiliar.

Giving students the tools and confidence to formulate practical solutions to problems, this book offers many useful features, including:

Learn more about Professor Ogunnaike and his work on the CRC Press Ning page. ... Read more

Product Description
Fully worked solutions to odd-numbered exercises ... Read more

Customer Reviews (29)

Great book for beginning
This book is great if you have no knowledge about Statistics and Probability. It clearly explains ideas and after every new theorem it includes many solved examples. The only thing i am unhappy with this book is it doesn't have solutions of end of chapter exercises but you can still buy solution's manual if you want.

stat book solutions manual
this comment refers to the previous edition of the walpole textbook solutions manual, not the most recent edition (2007?). it has been helpful. especially when the problems require the combination of 2 different probability distributions. most problems i've been assigned have been straightforward. but when you're stuck, and it's an odd problem, seeing how it's done can be very helpful.

No errata???
I'm floored that I'm shelling out $100 for this textbook and the publisher make *no* errata available for download.I've been using this textbook for all of a week, and I've already found two mistakes in chapter 2 alone.Chapter 2 is stuff I already *know*.What am I supposed to do for material I'm learning?

Helpful
This book does a pretty good job of explaining the problems and how they got the answer. I would definitely recommend it.

Terrible
This book manages to efficiently and masterfully destroy any shred of interest and enthusiasm that a student may have for the subject of mathematical probability and statistics. I took a probability and statistics class where the teacher was in no way useful to me. As such, I was forced to rely on this book. I quickly gave up on attempting to obtain even a marginal understanding of the subject and resorted to blindly memorizing formulas and theorems in the hopes that I could deceive the grader into thinking that I was actually competent with the material. I ended up doing well in the class, but despised every moment spent working towards that goal. This book would probably be useful as a reference for somebody already familiar with the subject, or looking to brush up on the material. However, I cannot, in good faith, recommend this book to anybody attempting to learn this subject. ... Read more

Product Description
Virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential.The second edition of this bestselling text introduces probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. It also features expanded discussions of the analysis of variance including single- and two-factor analyses and a thorough treatment of Monte Carlo simulation. The authors clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition, was designed as both a reference and as a textbook, and it serves each purpose well. Ultimately, readers will find its content of great value in problem solving and decision making, particularly in practical applications. ... Read more

Customer Reviews (11)

Review by a student user!
This book is mainly targeted for technical professionals such as scientists and engineers. It is well structured each chapter begins with table of contents of its own which I personally like because it adds to clarity of the material and one can easily locate the relevant topic easilywithin a chapter without having to waste time. Furthermore, it introduces new topic with theory and relatively good explanation of the equations which are followed by good examples. There are lots of graphs and tables which is great because it aids visual learner like myself and the majority of us are visual learners.
There is an additional feature included in this book that one does not find in engineering books and that is to emphasis the application of the theory to real world projects. This is supplemented by the introduction of projects that continue throughout the book to aid students learn the content. This book can be used by the students or professionals to aid them in problem solving or decision making depending on the users need. Often engineers and scientists have to make a decision based on an incomplete set of data, this book provides tools and method to aid such decision. Furthermore, the topics and methods covered in the text aid in decision making and also in determining likelihood of an event occurring. These types of information are often needed to make an educated guess, if the data is missing or hard to obtain. It also goes over the probability, that is likelihood of something occurring, an event based on the historical data.
I would have liked to see more and variety of examples after the introduction of a new topic. Furthermore the book doesn't contain answers to any of the problems that appear at the end of each chapter which is an issue. Most engineering books contain answers to at least odd number of problems but this book contains none. It is reasonably priced however I feel that color should have been used in the text to add a visual appeal to the book. Moreover, most books contain a CD to explain complex engineering problems and that component can be added for this text as well.
This book is great because it lays a foundation for engineering students and provides them with a great background in interpreting and analyzing data. Overall content wise this book is excellent which can be easily made better by adding the components that I mentioned above. I sincerely hope that this review aids in making this text better.

Probability, Statistics, and Reliability for Engineering and Scientists Review
I found this book to be helpful in learning probability and statistics. It is generally well written with clear examples of what to do. Some of the text gets a little wordy however and is hard to follow. The examples are usually easy to follow, and I appreciate the consistency of examples from chapter to chapter, and examples that are useful for real life application. The consistency of chapter organization is also a plus. I found it easier to read through a chapter knowing the basic outline of it and what to expect from section to section. The table of contents at the beginning of each chapter is a quality that in my experience is unique to this book. It is helpful to get a feel for the chapter before you start reading, or to quickly jump to the section that you need to find. It is also useful because it is easy to get lost in the numbering of the sections each with a 3 digit identifying number. The problems are mostly well written and applicable to the chapters. They don't follow the examples as closely as I would have hoped, but that probably makes for a better teaching tool. The simulation problems look interesting for more real world application. However, the book would benefit from a section with selected answers in the back. These are useful to let the reader know if they are on the right track or not. Overall this is an excellent textbook that is very well organized, it is hard to follow at points, but its strengths ultimately outweigh its weaknesses.

Good Reference Book
The purpose of this book is to inform engineers and scientist of how to use probability, statistics, and reliability in their respective field.
This book is well organized by chapter and subsections using numerals. Its organization first introduces a new concept, then proceeds to describe it in further detail, and finally gives applicable examples of how to use these methods.

This book can provide to be a useful tool and reference to its intended audience. It is easy to find different methods and each method is well introduced with its intended purpose. I personally felt the book could be gaged for a broader audience with a small amount of added descriptive detail. For someone familiar with probability, statistics, and reliability methods and someone using this book as a reference, I feel the book is very effective. Not being an expert and not having a large amount experience in the study of probability, statistics, and reliability, I believe it would be beneficial for the book to provide a few less-technical descriptive terms. As a very basic example: The book defines the use of the cumulative mass function in this way: "The cumulative mass function is used to indicate the probability the random variable X is less than or equal to xi." This is a good definition for someone familiar with the concepts and should not be omitted. For a novice, the inclusion of an additional sentence for clarity might help. To drive the point home it might include a following sentence such as, "For example, the cumulative mass function is the chance of picking a card out of a deck that has a number on it less than or equal to 3." This describes an action the reader can relate to. As the chapters move into more and more technical detail, a plain language, nontechnical description of what the general subject is can be very helpful for a novice reader.

Overall, this book is a well written resource for scientists and engineers. With the addition of a few nontechnical details, I believe the book will be able to reach a broader audience and be more applicable to students and the less informed reader.

Book Review
Probability, Statistics, and Reliability for Engineers and Scientists, written by Bilal Ayyub and Richard McCuen, is a very thorough textbook that covers all the important parts of probability and statists needed for engineers. It gives many accurate and applicable examples to its readers. All the examples are worked through very nicely, making it easy to follow for someone very new to the subject.

I would prefer if the examples are better highlighted and recognized as examples, to stand out more and draw the attention of the users that are searching for only examples. That being said, maybe adding some color, although might be more expensive, may help identify them. Also, if the spacing were a little larger in between sections it would not be as visually overwhelming.

The graphs are large and labeled really well and are often a great tool for the students to further their understanding of the material.

I like how equations are clearly written out and given a lot of room in between and after the text, making them easy to comment on with hand written notes in the margins. I also like how the equations are each numbered, making them easy to reference. I would prefer if the textbook were a little larger with respect to its surface area, thereby being overall thinner. I think this design would make the textbook more comfortable to hold and read through.

The amount of examples and review problems for each chapter make it really helpful to prepare for exams. I think it would be more beneficial if there were selected answers/solutions in the back of the book. I am often hesitant to do problems when I am not sure if they will be right, than again as it is now, it encourages students to study in groups. There are clearly arguments for both sides.

Before each new topic there is a very helpful in depth description and background of the topic to be stated that really helps the reader get the entire context of the topic. Without these sections I feel that I would be very lost.

As it says in the beginning of the text, the purpose of this book is to "1) introduce probability, statistics, reliability, and risk methods to students and practicing professionals in the engineering world and the sciences, 2) emphasize the practical use of methods, 3) establish the limitations, advantages, and disadvantages of the methods". Based on my observation, I think the goals set for this text were well achieved.

I do really like the textbook, and I think I get a very thorough understanding of the material, while seeing real life application. I would say it is a textbook worth keeping after this course as a reliable reference.

Comprehensive, Concise and Well-Written
One of the greatest facets of the textbook is the clear and concise writing style.The authors present the subject manner in a way that is both easy to understand and comprehend.A strong emphasis was placed on incorporating "real world" case studies within each chapter.This allows the reader to understand the significance and relevance of the subject material in applied engineering.Examples include [but are not limited to] building/construction design, and natural hazards.

The structure and setup of each chapter aids in achieving greater reader comprehension.Each chapter begins with an introduction, providing a brief synopsis of the material to be covered.The format is deductive in nature, beginning with general topics and moving towards specific focus areas.This format is particularly organized and systematic.

An important feature of the textbook is the incorporation of the derivation of particularly useful equations.All too often, in many textbooks, the derivation of equations is left out, leaving the reader with the "final equation" only.I believe that the inclusion of derivations allows for greater comprehensiveness.

In terms of improvement of the textbook, a larger number of pictures and diagrams [when necessary] may help, although it may be difficult to add.Additionally, the availability of a soft-cover copy would reduce the cost of the textbook.

In summary, this is an excellent source of information for understanding probability, statistics, and reliability. ... Read more

Product Description
This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years.  It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis.  Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines.

Key features:

• Presents the fundamentals in probability and statistics along with relevant applications.
• Explains the concept of probabilistic modelling and the process of model selection, verification and analysis.
• Definitions and theorems are carefully stated and topics rigorously treated.
• Includes a chapter on regression analysis.
• Covers design of experiments.
• Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields.
• Includes an accompanying Solutions Manual for instructors containing complete step-by-step solutions to all problems.

Customer Reviews (2)

Good Source of Information about Probability and Statistics
I purchased this book as a supplement for my Graduate-level course. It covered much of the "Proof left as an exercise for the reader" material in the course text. I work with probability and statistics and I use this book as a back-up for when I forget or encounter a problem requiring knowledge of statistics more obscure than Mean, Median, and Mode. The author has abbreviated proofs for many formulae. The examples are clear and support the material. An Instructor's Solution Manual would be helpful for autodidacts like myself. This book is well worth the price. I recommend it for business people who need it for work and for graduate-level students in a course on probability and/or statistics.

Fundamentals of Probability and Statistics for Engineers
A more advanced text on Probability and statisitcs which goes into more
advanced and complex detail on most topics than the standard undergraduate
probability and statisitics texts. ... Read more

Product Description
This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis. ... Read more

Customer Reviews (11)

Alright...
I had to have this book for a STAT class I take.Its an alright book.It gives decent examples and explanations of the concepts.But a lot of the answers in the back of the book are wrong, so be careful!

a standard statistics book for engineers
I taught a class at upper undergraduate level (mostly junior students) for a non-majors (mostly engineering students, some economics and political science) at a research university using this book.

It was written in the mid 1980s, and has not seen major updates even though it now comes in 4th edition. One of the assignments I gave was to comment whether you recognize the brand names of the mainframe computers mentioned in the problem. Newer stuff like say the bootstrap or machine learning or anything like that is not mentioned anywhere nearly. True, it has all the major results and techniques, but it was written by statisticians as the first course in statistics for to-be-statisticians, rather than the-only-course-in-statistics-you'll-ever-see for engineers. Thus a lot of things could have been presented in a different way with a different depth of exposure. Say the reliability, arguably a more important topic for engineers than moment generating function techniques, deserves a whole separate chapter, rather than being stuck in a middle of the chapter on continuous distributions. Simulations could have been highlighted throughout the book -- a good fraction of my students would probably be geekier than me with computers. I would unite all the confidence intervals under the umbrella of a single chapter, rather than presenting the CI for mean in one chapter and CI for variance in the next one. And so on. There even were errors in the answers in the end of the book, although you would probably expect the fourth edition not to have any.

Students complained a lot about the book in my class, too. Some said it did not help much, although there were others who did not come much to class (admittedly, I am a pretty boring lecturer) and got B's and A's, so apparently it was of some use to them. The price is of course also an issue: I personally won't pay $120 for book of this quality to sit in my professional library, and it sucks that I have my students buy it.

[Wasserman's [ASIN:0387402721 All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)]] is a much more modern book, although in all likelihood it would be difficult for my clientelle. My other favorite is Utts' Seeing Through Statistics (with CD-ROM and InfoTrac ), but this one is on the other side of technicality, being too easy. Finally, for engineering students specifically, Ryan's Modern Engineering Statistics (Hardcover) appears to be a much better text, although I have not taught from it, and my recommendation is based on just browsing through the pages and supplementing the current book with examples and problems from Ryan's book.

Pretty Solid
For what it is, it's pretty good.Very well organized, and easy to read (a little dry, but this isn't exactly a recreational novel).Four stars only because it wouldn't hurt to help us use some modern tools (calculators, computer software, etc), instead of the tables in the back.Maybe that's changed since my 3rd edition.

Very Good as Textbook
This book was recently used as a textbook for an engineering statistics class.Many of the students liked the book and found it easy to read.The level of mathematics in the book is excellent for a college level statistics textbook.I would have given it a five star rating if propogation of error and nonlinear regression analysis were covered in the textbook.

not entry level
This isn't entry level book.For entry level, read ISBN 0073660078 first.Then read Milton's book. ... Read more

Product Description
An up-to-date, comprehensive account of major issues in finite mixture modeling
This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts.
Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-400ublished since 1995
* Includes an appendix listing available mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data. ... Read more

Customer Reviews (5)

excellent coverage of mixture models and likelihood inference with EM algorithm applications
McLachlan and Basford (1988) and Titterington, Smith and Makov (1985) were the first well written texts summarizing the diverse lterature and mathematical problems that can be treated through mixture models. Geoff McLachlan is the author of four statistics texts namely (1)McLachlan and Basford (1988) "Mixture Models:Inference and Applications to Clustering", Marcel Dekker, (2) McLachlan (1992) "Discriminant Analysis and Statistical Pattern Recognition", Wiley (3) McLachlan and Krishnan (1997) "The EM Algorithm and Extensions" Wiley and (4) McLachlan and Peel (2000) "Finite Mixture Models" Wiley. These four books are all related to the interesting problems in pattern recognition and clustering. Mixture models and the EM algorithm are tools used to solve problems in clustering and pattern recognition.

In each of his books McLachlan has shown an ability to be clear, authoritative, scholarly and thorough. He provides broad coverage of each topic with detailed references. This book is no exception. As he point out in the preface, the literature on mixture models has expanded tremendously since the appearance of his 1988 monograph with Kaye Basford making an updated text very appropriate.

Almost 40% of the 800 references in the text have appeared since 1995. The recent advances covered in the text include identifiability problems with mixture models, the analysis (fitting of mixture models) for real data sets using the EM algorithm and its extensions, properties of maximum likelihood estimators, applicability of asymptotic theory, use of bootstrap methods to assess accuracy of estimates, implimentation of Bayesian approaches through Markov chain Monte Carlo methods and the use of hierarchical mixtures-of-expert models for nonlinear regression as competitors to the MARS and CART algorithms.

This is a great book. Chapter 1 provides a nice overview of the subject with a thorough historical treatment, nicely presented in Section 1.18. In addition to the fact that it covers all the recent advances one can think of. The book also deals with fast implementations of the EM algorithm for data mining and other approaches to modifying the EM algorithm to handle large data sets. There is also a wealth of interesting real problems worked out in detail. These problems come from many disciplines, including interesting medical problems related to diabetes and hemophilia, nuclear test ban data analysis, image processing and competing risk survival analysis. It also covers some interesting aspects of multivariate normal mixture models and their applications.

Not Enough Details - - Assumes use of unavailable Software
This title is not up to the high standards set by McLachlan's other books. The text does not get into details which are thoroughly discussed
in McLachlan's excellent "The EM Algorithm and Extensions." Some important points are so condensed and in places so poorly presented that the the discussion is worthless. For example the important section 3.4 "Standard Errors", which underlies much of the discussion contains errors.
The book could be useful to a practioner who is only looking for guidance in fitting mixture models with available software.Unfortanately McLachlan's mixture software referred to in the Appendix, is not available. That's a pity because the advertised software features and design potentially rank it the top choice.
For a user who will use his own EM software to fit mixture models I would recommend highly the McLachlan's EM Algorithm bookover the Mixture book.

Excellent!
"Finite Mixture Models" is an excellent reading for scientists and researchers working on or interested in finite mixture models. It provides a comprehensive introduction to finite mixture models as well as an extensive survey of the novel finite mixture models presented in the most recent literature on the field in conjunction with the prospective practical applications of them. In addition, the book is very well-written and it has the merit of the use of a consistent mathematical notation throughout the book.

Wonderful!
A wonderful text that functions as well as a reference as it does as an introduction to mixture models. I was surprised by the depth and breadth of the book, which manages to describe almost every mixture model imaginable and then some more, including forms of the models themselves, parameter estimation and fit. Relationships between different models are made clear, lending the text a coherence that isn't undercut by vague generalities. The authors are particularly good at addressing issues of particular importance in mixture modeling, such as fit and model selection. Material is suprisingly recent as well. Overall, a great text that is probably destined to become the standard reference on mixture models.

Job well done
Mixture models have become a hot topic in statistics. After you read this book, you will know why.

"Finite Mixture models" have come a long way from classic finite mixture distribution as discused e.g. Titterington et al(1985). A small sample should almost surely entice your taste, withhot items such as hierarchical mixtures-of-experts models, mixtures of GLMs, mixture models for failure-time data, EM algorithms for large data sets, and hidden Markov models.The book gives a lucid overview of recent developments on mixture models since 1990 (the aim of this book in the first place). It expounds on the modern viewpoint that mixtures can be usefully exploited as a mechanism for building flexible statistical models for complex processes, e.g. nonparametric Bayesian models. Balanced attention is given to all three modern approaches to fitting mixture models which include speed-up EM, Bayesian, and stochastic simulation. The whole book is superbly written,and very entertaining---It's hard to put it down once started. It is very update with 45 pages ofreferences and an appendix listing available softwares.

I'm a big fan of Prof. McLachlan's books; and I believe, this latest book of his with one of his student D. Peel, should addanother masterpeiece to the long list of marvelous statistics books coming out of Australia and New Zealand... ... Read more

Product Description
This comprehensive introduction to probability and statistics will give you the solid grounding you need no matter what your engineering specialty. Through the use of lively and realistic examples, the author helps you go beyond simply learning about statistics to actually putting the statistical methods to use. Rather than focus on rigorous mathematical development and potentially overwhelming derivations, the book emphasizes concepts, models, methodology, and applications that facilitate your understanding. ... Read more

Customer Reviews (46)

Very bad service
I had ordered this book on Sep 8 2010 and immediately sent an email to the seller asking about the condition of the book, but got no response. Then sent another email asking for order status, but still got no reply from the seller.

Then after waiting for a week, I decided to cancel the item and file a claim for my refund.

I was enrolled in a class and needed this book for that class. But because of this seller's laziness I lost a week being without a book to study.

So my sincere suggestion to anybody wanting to buy from this seller is to look some place else.

I am really upset that I had lost a week during my course and more sad that the seller did not even have the courtesy to reply to my mails.

Enjoyable statistics book
I have found this book (an older edition) to be an enjoyable introduction to calculus-based statistics.

The best introductory text for people who actually want to understand statistics
If all you're looking for is "how to plug numbers into formulas," this isn't the book for you.But if you want to understand WHY you're using a particular formula to solve a particular problem, this is probably the best introductory statistics textbook out there.Highly recommended not only for would-be statisticians, but also for students in science, engineering, medicine, finance, etc. who want to know if their results make sense.

wrong edition
the student solutions manuel edition that was sent to me was not the same as the edition it was advertised under! the isbn numbers dont match!

Never buy from Woody's Book again
Not only did I never get the product but when I had to buy it from the bookstore it cost me 80.00 more. Thanks alot Woody's books. If you had just been honest and upfront with me, I would have bought elsewhere. ... Read more

Product Description
With interest growing in areas of forestry, conservation and other natural sciences, the need to organize and tabulate large amounts of forestry and natural science information has become a necessary skill.Previous attempts of applying statistical methods to these areas tend to be over-specialized and of limited use; an elementary text using methods, examples and exercises that are relevant to forestry and the natural sciences is long overdue. This book utilises basic descriptive statistics and probability, as well as commonly used statistical inferential tools to introduce topics that are commonplace in a forestry context such as hypothesis texting, design of experiments, sampling methods, nonparametric tests and statistical quality control.It also contains examples and exercises drawn from the fields of forestry, wood science, and conservation. ... Read more

Product Description
This is a new edition of a very successful introduction to statistical methods for general insurance practitioners.No prior statistical knowledge is assumed, and the mathematical level required is approximately equivalent to school mathematics. While the book is primarily introductory, the authors discuss some more advanced topics, including simulation, calculation of risk premiums, credibility theory, estimation of outstanding claim provisions and risk theory. All topics are illustrated by examples drawn from general insurance, and references for further reading are given. Solutions to most of the exercises are included. For the new edition, the opportunity has been taken to make minor improvements and corrections throughout the text, to rewrite some sections to improve clarity, and to update the examples and references. A new section dealing with estimation has also been added. ... Read more

Customer Reviews (1)

Good overview
For those who study actuarial/statistics this book provides good overview of characteristics of poisson, variance and standard deviations. Full of worked examples this pocket sized book is recommended by actuarial academics alike. ... Read more

Product Description

This book provides an introduction to probability theory and its applications. The emphasis is on essential probabilistic reasoning, which is illustrated with a large number of samples. The fourth edition adds material related to mathematical finance as well as expansions on stable laws and martingales.

From the reviews: "Almost thirty years after its first edition, this charming book continues to be an excellent text for teaching and for self study." -- STATISTICAL PAPERS

Customer Reviews (5)

Probability
Did not recieve the item in two days when I paid extra for expedited shipping.Recieved it 3 weeks later

A very good book indeed
A reviewer who has given this book 2 stars below says "It was the only math textbook that I really struggled with". That tells me this guy shouldn't have been in a maths course in the first place; stick to drama or English dude, they may be a little taxing for your brain. There should be a rule on Amazon against letting idiots review books of substance.

Chung's book is rich in historical notes, motivation and intuitive development of the subject. Chung is a world-renowned probabilst and also is a gifted writer. This book was followed in my undergraduate days and I still have fond memories of it.

My only gripe is that the new edition has added stuff by another author on finance. There was no need for this whatsoever.

best textbook for elementary probability theory
As a professor in computer science, I am teaching a seminar course in which I wanted to cover basic probability theory in a week.I read at least a half dozen textbooks in the university library and found this book to be far better than others for my purpose.In particular, the material I used was the derivation from the binomial distribution (a coin toss) to the normal and the Poisson distributions, which I covered in two classes.Students liked the many interesting, real-life examples in the book. In addition, I taught the two proofs for the law of large numbers.The second one from Chebyshev was more powerful (applies to non-identical distributions), stronger (guaratees the speed of convergence), simpler and shorter (half a page with no need of mathematical analysis).It eclipsed the theories of other mathematicians in the previous 200 years.The Chebyshev's theorem was new to me and to all the people I mentioned this to.

Of the books I know, this is the best entry level textbook for probability theories.I did not read the chapters on mathematical finance.

Great thoughts in every page
I just read the review by another reader, I would say unfortunately he was wrong. This book is one of the greatest probability book I have ever seen. If you want high-school level combination problem, this book is not for you. But if you want the essence of probability theory, this will be the perfect book for the entry. Actually I'm annoied by the comments of the other reviewer. I think he needs to review himself if he is not competent enough to take such course.

painful... even by undergraduate math textbook standards
I remember this ghastly nightmare from my undergraduate days. It was the only math textbook that I really struggled with. Part of that was probably due to having an inordinately lousy professor, but part of it is because the book reads more like a quick review for people who already know the subject matter than as an actual tool for learning.

As a contrast, check out what people are saying about "A Book of Abstract Algebra" by Pinter -- they're right, THAT is everything a math textbook should be. My class never quite finished it, but I had no trouble reading the later chapters on my own. I still have a copy of Chung's book, but it only has one remotely interesting thing in it that I remember, which was Laplace's calculation of the probability that the sun will rise tomorrow.

Bottom line: if you're unfortunate enough to end up with a professor who is still using Chung's book (I used it in 1997) ... run! ... Read more

Product Description
These books have been specifically written by experienced authors for the option modules in the new Higher Level IB Mathematics syllabus. Each book has been thoroughly reviewed by IB teachers and contains detailed explanations, plenty of questions, a review section and past-examination questions. ... Read more

Product Description
Remarkable selection of puzzlers, graded in difficulty, that illustrate both elementary and advanced aspects of probability. Selected for originality, general interest or because they demonstrate valuable techniques, the problems are ideal as a supplement to courses in probability or statistics, or as stimulating recreation for the mathematically minded. Detailed solutions. Illustrated.
... Read more

Customer Reviews (11)

Fun and challenging
I have enjoyed going through the problems in this book. Sometimes I would get out a pencil and paper to work through a problem, and other times I would just skip right to the answer. The solutions are well explained and easy to understand.

A great treasure
I don't know what to say. The problems here are really interesting. The problems range from very easy ones to very difficult ones. There are problems on variety of prob topics including continuous prob. Some of the problems are very hard to the point you want to pull your hair out. However, it is definitly the best problems I have seen.

happy with the purchase.
Interesting book, challenging for anyone who likes to work on math.

For math lovers
I got this book because I wanted to strengthen my skills with basic probability as it relates to gaming, gambling and trading.

In some areas the problems here were helpful to me, and the solutions are well explained.

In a few areas the problems were way over my head and in areas I either don't understand or am not interested in.

That said, I think anyone with an interest in the subject won't be disappointed with this thin book, except maybe wishing there was a greater variety of problem types, which wasone complaint I had, the second was that the solutions were a bit too complicated, which may mean I'm just not smart enough, however, it would have been a bit stronger of a book if it had explained some of the items at a lower level (for us numbskulls).

Worth its' weight in a very precious metal
This collection of fifty-six classic problems in probability is a first-rate work. All of the solutions are well written and easily followed. The reasoning is general enough to allow you to go on and solve related problems. Examples are birthday matching, trials until success, cooperation, gambler's ruin, and Buffon's needle.
If you have a soft spot for problems in probability, this book is an inexpensive must.

Published in Journal of Recreational Mathematics, reprinted with permission. ... Read more

Product Description
We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications.

Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by:

• Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis.
• Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book.
• Introducing the notion of “composite reliability”, or the collective reliability of a population of items.
• Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk.

Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems. ... Read more

Customer Reviews (4)

excellent book
Professor Singpurwalla is a well-known statistics professor with expertise in both the applied and theoretical aspects of reliability.Many years ago (around 1974) he coauthored with Nancy Mann a text on reliabilty that is now viewed as one of the classics.That book dealt primarily with parametric models and parameter estimation and hypothesis testing as tools in reliability analysis.It used the the standard frequentist/likeihood approach.Dr. Singpurwalla now adheres to the Bayesian approach to statistical inference and this is reflected in his many research papers and recent books.This text is an excellent modern text in reliability taken from the Bayesian perspective.It is probably the best book out there right bow that embraces the Bayesian approach.Other works are the recent books by Richard Barlow and the old text by Ray Waller.Waller's book was mainly applied and emphasized examples from the nuclear industry that he worked in at that time.

Dr, Singpurwalla is an excellent and very experienced writer and this book can be used both as a reference and a text in Bayesian reliability.

The product of clear thinking and experience
You know that this is a book with a difference from the start, when the second and third chapters devote themselves entirely to justifying why probability, utility, exchangeability and indifference are the fundamental ideas behind risk and reliability.

Never heard of utility or exchangeability? If you do any Bayesian statistics at all, then shame on you and read Chapters 2 and 3 immediately! If you do not, then do not let this put you off reading further. This is a book on reliability with impressive intellectual weight. It not only tells you the what and how of reliability and risk analysis using Bayesian methods, but also the why. The why is a philosophical and a mathematical argument. For example, you think you know what the failure rate is? Prof. Singpurwalla begs to differ and spends 10 pages to convince you that there is a lot more to the failure rate once you start to think about it. You think you know why the exponential or the Weibull should be used? Well, the simplest assumption that you can make about any set of objects is that you are indifferent between them and such indifference can lead you to use the exponential or Weibull as models for reliability. This is the idea behind exchangeability and Prof. Singpurwalla argues why it is the simplest way to think about modelling and why it then justifies using Bayesian statistical methods.

The rest of the book is a plethora of probability models and inference methods for different problems in risk and reliability. Those models and problems are a summary of the author's work in reliability over 4 decades. There are chapters on stochastic models of failure (including the discussion on failure rates), on how to do Bayesian statistical inference for the common lifetime models such as exponential and Weibull, as well as common topics like accelerated tests and dose-response experiments and a chapter on signature analysis that describes the analysis of the power spectum for reliability. Then there are chapters look at survival in dynamic environments, point processes for events and non-parametric Bayes methods; technically more advanced topics that require a good knowledge of probability theory but, as always, clearly developed and explained. The last two chapters look at relatively new and 'hot' topics, those of the reliability of co-operative, competing and vague systems, and the use of reliability in econometrics and finance.

If you're looking for a book of reliability analysis recipes that you can follow without thinking, then this is not the book for you. If you want a book that carefully lays out a logical approach to risk and reliability modelling, inference and prediction, at a good yet clearly explained technical level and that illustrates the approach with applications to many different reliability problems, then this is the book that you've been waiting for.

Excellent Textbook from a student's point of view
This is the prescribed textbook for my Reliability & Risk Analysis class. I found this book to be very student-friendly and each topic is well motivated.

For beginners (like me), this book is a Gold Mine !!!

Agreat book by Nozer Singpurwalla
The book by Nozer Singpurwalla provides a clear introduction to the definition of "Probability", and the foundations on which the Probabilistic Framework is built. In almost every facet of today's life, Probability is dominant as a used concept. Although seemingly trivial to define in some simple cases such as cards or flipping a coin, much controversy prevailed over centuries on the topic of what is exactly meant by "Probability". The author Singpurwalla provides an extremely clear and abundant discussion of the subject, that is relevant at the introductory level as well as at the top level of the scientific community. This introduction is done in the context of Reliability and Risk Analysis, which makes it even easier to comprehend than in a more abstract setup. The book is worth buying on the basis of the second chapter alone "The Quantification of Uncertainty".

For the reader that is more versed into the subject, the author takes it to the highest level by introducing "Exchangeability and Indifference", a much specialized topic.

The author Nozer D. Singpurwalla being an expert authority in the subject of "Reliability", the crux of the book comes in chapters four (4) to ten (10) that offer a comprehensive review of the material in the field. The value of these chapters is in the author's expert selection of all the areas of value in Reliability and Risk Analysis. While many standard books present a linear listing of most techniques, Singpurwalla presents a collection of approaches that are classified through ther conceptual content and are meant to take a novice student to the expert level. While some of these concepts are explained in details, others are too complex to be detailed and are properly referenced. Of particular interest to an expert would be the part on the use of stochastic processes for modelling reliability. Not that many have attempted such a road.

Finally the last chapter introduces some of the Risk notions to the finance world. Although such a world is terribly complex and not so easily prone to successful modelling, this chapter nevertheless will open the avenue to fresh minds on how the Probabilistic Framework can be applied in diverse areas.

Throughout the whole book, a decision theoretic approach is outlined either explicitly or implicitly, as prescribed by the Bayesian paradigm.

Definitely an excellent contribution by a top expert. I was not surprised at all that three top scientist, Lindley, AFM Smith and Kadane offered their view on the book cover. A top book.
... Read more

Product Description
The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models.Lee J. Bain and Max Engelhardt focus on the mathematical development of the subject, with examples and exercises oriented toward applications. ... Read more

Customer Reviews (12)

The Horror The Horror
God help you if you are inflicted with this book for a class on mathematical statistics. You'll really need to depend on your professor's lectures to learn anything. As other reviewers have said, the examples are too easy and the problems are too hard. Be sharp on your algebra and calculus, as you're going to need it - many solutions require difficult integrals and rarely used algebra theorems. In addition, the examples in the text are terse and not explained very well. Often an example will have a statement like, "Obviously, since A=B, then" - well, it isn't obvious that A=B and you have to spend 5 minutes figuring that out (often referring to prior chapters). Or they'll present a proof and not explain the steps - again, more time figuring out what the heck they're doing. As for the problems - very hard - and often you have to spend a lot of time just trying to figure out what is really being asked. Probably a great book if you already know mathematical statistics, but if you're trying to learn it for the first time . . . run away! run away! The worst mathematics book I've ever used - even worse that Advanced Engineering Mathematics by Kreyzig.

Exam P
I bought this book for my BYU STAT470 class.The class is designed to prepare you for SOA Exam P, but I found the book hard to navigate and really doesn't offer much in the form of exam practice.I feel that the ACTEX manuals are a much better resource for learning Exam P material.

Good book
The book is a good intermediate book.I was able to build off this and reference this book in my other mathematical statistics courses.

The quality of the book was as described and it shipped quickly.

Is it really that bad?
I'd like to say that this book, while hard, is a good book. It goes through semi-simple examples and then leaves the student to work through a bit more difficult problems that gives the ability for the student to expand his or her mind in the material, which is needed in a mathematical field. However, I definitely would not suggest it as a book to learn from on your own just for this reason because you really need a teacher to go to and ask questions. I liked the book because it goes way more in depth on the theory than others I have looked through, so beware if your math skills are not up to par. Just to let you know, we used it for a half undergrad/half graduate course at my school and this was my first class in statistics. It wasn't horrible.

Graduate Probability and Statistics
This book is great for graduate studies in Probability and Statistics. However, you will need some mathematical maturity because some of the exercises contain mathematical proofs involving Calculus. I must admit that this book could be challenging at times, but that doesn't mean one should degrade the book. I like this book and I highly recommend it. You will obtain a great foundation in Probability and Statistics after using this book. I actually used this book as a text during my undergraduate studies, but I think only advanced undergraduates can handle it. ... Read more

Product Description
This concise book for engineering and sciences students emphasizes modern statistical methodology and data analysis. APPLIED STATISTICS FOR ENGINEERS AND SCIENTISTS emphasizes application of methods to real problems, with real examples throughout. ... Read more

Customer Reviews (5)

Great value, i mean it
The book is new, with all the pages with new-book-smell. It is exactly as the picture. Compared to other places, this book is so freaking expensive. So, I strongly suggest you guys to buy it here.

Don't buy this book from green leaves
Do not purchase this book from third party seller "green leaves". I tried it because they use Amazon shipping. I paid for one day delivery. After 3 days I found out from Amazon help that the seller doesn't even have the book and Amazon is crediting my account. Great! Classes started yesterday and I am back to square one. And I can't even leave this feedback on the seller's page...
This seller gets NO STARS but I had to give them one just to post this.

good
i really don't have much to say. the shipping was pretty fast and this book is my class requirement.

Be careful, numerous errors
This book is full of errors.Not only in the text, but our class found that numerous answers to the odd problems (in the back of the book) were wrong.It became so bad that when your answer differed from the book's, your first instinct was to assume that your answer was right and the book's answer was wrong.(Not a good thing in a text book).And, since the book was published in 2005, one would think that the publisher would have made an errata available on their web site.My suggestion would be to look elsewhere for a better book.

Excellent statistic tool
Nice package of useful stat information, in a small and handy format.I'll hang onto this. ... Read more

Product Description
Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning.

Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables topics, including univariate and multivariate distributions. After exploring sampling distributions, the authors discuss point estimation, confidence intervals, hypothesis testing, and a wide range of nonparametric methods. With a focus on experimental design, the book also presents fixed- and random-effects models as well as randomized block and two-factor factorial designs. The final chapter describes simple and multiple regression analyses.

Demonstrating that R can be used as a powerful teaching aid, this comprehensive text presents extensive treatments of data analysis using parametric and nonparametric techniques. It effectively links statistical concepts with R procedures, enabling the application of the language to the vast world of statistics. ... Read more

Customer Reviews (4)

Not As Advertised - Give it Zero Stars!!
The section on R is poor compared to the many texts out there doing stat with R (just search 'Statistics with R' in Amazon.com and you will get, at least, five to seven 5 star books).Since the R part is poor, the stats with R part is even worse.Need a complete re-do.Actually, just give it a complete no-go.Just buy one of the more established texts 5 star texts - you won't go wrong.

Poor for learning R
I'm using this book to learn R for my PhD program.I can't speak to how well it does or does not teach statistics, since I am not really paying attention to that aspect of the book.What I can speak to is the fact that the book does a poor job of teaching one to actually use R.The "Introduction to R" chapter is just a hodgepodge introduction to how R treats data and basic linear operations.After that the book moves on to statistical concepts and how to use R in their application.The serious problem with the book is that there are many examples which require basic commands not covered in the introductory chapter.These commands, while present in the examples, are neither named nor explained.So instead of a pedagogical tool that teaches one how to actually use R, what you get is a huge book of examples that merely teaches you how to replicate the examples.

Solid book on probability and statistics
The book it's ok, but it has errors and does not include the answers to the exercises. If you want to learn the theory, its not that good.

Textbook with R applications
Probability and Statistics with R is a 700 page textbook about how to use R for probability and statistics. The examples do not skip steps.Extensive code is available either in the text or at the web site where scripts are available for most chapters. The book gives an intro to the R/S language, explores data with graphs and basic statistics, moves on to random variables and their properties, continues with both univariate and multivariate probability. It covers sampling distributions and uses those to create point estimators whose distributions help with the construction of confidence intervals and with performing hypothesis tests. Nonparametric methods, experimental design, and regression round out the text. Problems accompany every chapter. The index is extensive.

NOTE: The 500 page solutions manual is available to instructors who adopt the text, and an errata page is maintained where many of the typos from the first and second printings have been fixed.

Since I wrote a first draft of this review, the American Statistician (Raquel Prado, University of California, Santa Cruz, February 2009), Significance (December 2008, page 186) and TECHNOMETRICS (TECHNOMETRICS, MAY 2009, VOL. 51, NO. 2 page 22) have published reviews.Please read theirs if it bothers you that I am the wife of Alan T. Arnholt, one of the authors.(I do have an M.S. in Statistics from UNC-Chapel Hill; I was in charge of making sure the math showed all the steps; and I did type the bulk of the text in LaTeX--all the nice typeset equations were my doing; and I have taught out of more than a few textbooks, so I feel qualified to give the book 5 stars...but please read the longer published reviews if you are concerned about bias.) ... Read more

Product Description
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called ‘prior ignorance. The work is written from the authorss committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. The book will be an ideal source for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics ... Read more

Customer Reviews (8)

bayesian theory bible
Recently there have been a wealth of good books published on Bayesian methods and the Markov chain Monte Carlo approach to its implementation. For the beginner Berry's introductory book is a good place to start.
Bernardo and Smith are experts in the field who have participated in many of the Bayesian conferences held in Valencia and much of that lterature is contained in this book. They originally wrote the book in 1993 (with a publication date of January 1994). This paperback edition is not a revision but rather a reprinting with corrections. The original hardcover edition was very expensive and this paperback edition makes the text more affordable and should greatly expand the list of Bayesian specialists and other statisticians and practitioners that read it.

The authors intent was to extend the classical work of Bruno deFinetti who popularized the Bayesian approach with his two classic probability books. One of the authors was involved in translating deFinetti's books into English and they are both well familiar with it. In this book they offer an extension to the area of statistical inference.

The beauty of deFinetti is the logical and systematic nature of the presentation but he did not extend this to statistical practice. These authors maintain the systematic approach and review the probability axioms but then go on to cover statistical modelling including how models are approached through concepts of exchangeability, invariance, sufficency and partial exchangeability. The chapter on inference covers the Bayesian paradigm, the use of conjugate families, asymptotic methods, multiparameter problems and the thorny issues with nuisance parameters. It also includes a number of methods of numerical approximation including Markov chain Monte Carlo (MCMC) methods.

The authors deliberately left the coverage of computational methods brief as they planned a second volume to cover it in detail. But in the preface to the new paperback edition they admit that they have abandon this plan due to the evolution of MCMC methods as the dominant numerical approach and the wealth of new texts that adequately cover the topic.

I suggest that this text is the new bible for Bayesian statistics because I think it replaces the old bibles, Lindley's two volumes (some may argue for Savage's book). This is fitting as both authors attest to being students and disciples of Dennis Lindley. The reason I think it is worthy of bible status is because it covers the foundations in systematic detail, is current and very complete. The text contains references from 1763 (Bayes' original treatise) to 1993 covering an incredible 66 pages of the text. With 20 plus references per page that means over 1320 references!

This is an intermediate level text that requires advanced calculus but not measure theory. Emphasis is on concepts and not mathematical proofs. The authors also provide an overview of the non-Bayesian forms of statistical inference in Appendix B. The authors confront the controversial issues in each chapter. Bayesian statistical methods are treated in the framework of decision theory and ideas from information theory take on a central role.

Bayesian Theory
An excellent book.Three things I like: (1) it is correct (so many others are not), (2) it can be read by someone who does not have a PhD in math, (3) they don't pull punches.Appendix B explains directly why all alternative theories are nonsense.

Forrich, subjectivist true believers only
Bernardo and Smith(BS)have written a book that assumes thatFrank Ramsey, Bruno De Finetti,andLeonard Savage solved all of the major problems concerning the foundations of probability and decision theory in the period between 1931,the year Ramsey's major essay on probability was published,and 1954,the year that Savage published his book.All that remains is a mopping up effort at minor,residual anomalies.The basic point made by BS is that all probabilities are precise,single number, point estimates or that they can be treated "as if" they were.Unfortunately,this is not the case.The subjectivist approach is applicable only in those situations where the purely deductive,mathematical laws of probability(the addition and multiplication rules for conjunction and disjunction)apply.This requires that a)there exists a complete sample space of all possible outcomes representing the choice problem before any probability is calculated;b)a complete preference ordering of all possible outcomes exists for the problem or c)a single,unique probability distribution is defined for the problem.Under these conditions,the probability calculus serves as a consistency and coherence check for the rational decision maker who is willing to bet on one side or another of all propositions.The subjectivist approach is a special theory with limited applicability.It is this failure to recognize that the subjective approach is a limiting case, that conflates the concepts of probability,logical probability,inductive probability,and degree of belief with mathematical probability, that is the source of much of the criticism of the subjectivist approach.There are many assertionsmade throughout the book that are highly dubious and/or unsupported.The rest of the review will be devoted to correctingthese assertions.First,it is not the case that the Allais paradox choices are mistaken.It is strange to see it argued that such choices are similar to"...individuals(who)can often be shown to perform badly at deduction or long division"(BS,P.97).The real problem is that many/some decision makers have nonlinear probability preferences,as opposed to the linear probability preferences axiomatised by the subjectivists.The BS claim is similar to the claim made by many proponents of Euclidean geometry in the 18th and 19th centuries that non Euclidean geometries were erroneous and/or could not exist.Second,it is not the case that the Raiffa(1961) and Roberts(1963)replies to Ellsberg provide"...clear and convincing rejoinders to the Ellsberg criticisms"(BS,P.98).Both Raiffa and Roberts,like Savage in his belated reply to Allais,simply restructured and changed the problem on which they commented.Third,the claim that the Ellsberg problems and/or examples(the two color and three color urn ball problems)are"...optical or magical illusions..." makes no sense.Fourth,the claim that"The logical(emphasis added)view is entirely lacking in operational content." (BS,p.100),has no support at all.It is impossible to even talk about scientific theories unless an underlying logical conceptualization of probability is already in place beforehand.Fifth,the claim that John Maynard Keynes changed his view in 1931 and accepted the primacy of the subjectivist interpretation of F.Ramsey is erroneous.Keynes accepted Ramsey's dutch book argument claim only if the deductive,purely mathematical laws of probability("...the calculus of probability...") were completely operational.Keynes completely rejected Ramsey's assertions that habits and memory alone were theonly foundations for induction and analogy.Sixth,BS are completely and totally ignorant about Keynes's establishment of the interval estimate approach to probability in this century.It is a widespread misbeliefon the part of many economists,philosophers,psychologists,etc.,that only partial, ordinal rankings,that could be made only part of the time,representsthe main outcome of Keynes's 16 years of study of probability.Nothing could be further from the truth.In fact,this misbelief is due to the acceptance by most scholars of the conclusions arrived at in the horrible mess made of Keynes's book by Ramsey in both his 1922 and 1926 reviews,respectively.Ramsey's unsupported claims about Keynes's strange nonnumerical probabilities and mysterious logical relations are just that,unsupported.Most Keynesian probabilities have an upper and a lower bound or limit. It is in chapters 15 and 17 of Keynes's 1921 A Treatise on Probability(TP) that BS can find Keynes's "approximation" approach worked out in great detail.A number of problems are worked out by Keynes on pp.161-163 and pp.186-194 of the TP.All of these problems can now be solved using easier integer-mixed integer linear programming techniques.Keynes's approach is fully operational.Seventh,the claim that Keynes'slogical approach provides "...no operational guidance as to how to choose..."(BS,p.99)makes it crystal clear to this reviewer that BS have never read Keynes's TP.It is a great tragedy that books can be written on probability by authors that are grossly ignorant of basic literature.

A complete introduction to classical Bayesian analysis
[1] It is an excellent book on the classical Bayesian theory. The first author is a famous mathematician, who held several international conferences on Bayesian statistics.
[2] Similar to Berger's book, it is also built on Statistical Decision Theory. In my opinion, Berger's is a little better.
[3] The part of Bayesian foundation is heavy, maybe a topos today. But in the bookshelf, we indeed need such work.
[4] Think about the thickness of the bibliography --- the reference is awesome!
[5] The history of Bayesian statistics is well overviewed.
[6] To learn more about the Bayesian computation, you need some complement books, such as Liu's, Tanner's, Gelman's, etc.

Nice but....
Really nice book, but a VERY expensive "bible" if you ask me. $300, what a joke. ... Read more

Customer Reviews (1)

Junk book
Very bad in organize contents and explainations. I thought i'm not good at probability until I read another probability book i borrowed from my friend from another class, and since then my grades and my understanding the materials rapidly improved. Of course I can not borrow my friend's book all the time, and whenever I come back to this book, I'm totally lost again.
You can go ahead and waste your money in this book and correct me anytime, but I do feel bad to resell this book since this is a piece of junk book.
Every chapter has some incorrect answer, chapter 2 has 1, chapter 3 has 1, chapter 4 has 2, chapter 5 has 1, and all of them are odd numbers, so if you cant do some question which is ridiculously easy, dont feel so bad, but move on to the next one.
Such a waste of money and time. Can't believe my university chose this book as standard. ... Read more