I remember the first time I touched this book, i fell so in love with it that it was very hard for me to remember how many other things I had to do during my day. It really illustrated how every problem you solve (or at least try really hard) can be an entire lesson you can use later on.
It is very well organized, even the problems in each section are set in a way that each one helps with the previous one in case a more creative solution doesn't show up...
I love this book, and I really recommend it for any student studying for any math contest around the world. It really helped me, and I'm sure it will do the exact same thing to anyone with the desire to spend countless hours solving beautiful math problems. Good luck, God bless you all :)
Pura vida.

This is a marvelous book for lovers of mathematical problems. Scattered about are wonderful problems in Geometry, Trig, Algebra and Analysis, Invariants, and Number Theory. A truly delightful read that will have you working on some problems for hours. Each section introduces the reader to the concept or technique needed to solve the problems in each section. The problem sets start off with a few "warm-up" problems that quickly build up to some that require keen (some brilliant!) insight. A true gem among most problem books since this book is not merely a book of problems, but also contains clear presentations and introductions to various concepts in mathematics. The solutions are a true delight, the ingenuity and beauty of mathematical problem solving is captured exquisitely in this fabulous book. Highly Recommended. A++

The book, Mathematical Olympiad Challenges", is a delightful book on problem solving written by two of the leaders of the craft. Mathematical problem solving is a skill that can be honed like any other and this book is an ideal tool for the job. Problem solving usually involves elementary mathematics; this does not mean "easy mathematics". An elementary mathematical problem is one that is easily stated and can be understood by anyone who has had basic training in the subject (up to calculus). The solution, though, may be quite hard and may require a great deal of ingenuity and thought.

It should be noted that being an exceptional problem solver does not necessarily make one a good mathematician, but it helps. This is certainly true of the second author who is also a renowned mathematician in the field of knot theory and three dimensional topology.

As mentioned the two authors have a sterling record in the arena of problem solving and in coaching would be problem solvers. I am more familiar with Razvan Gelca who led the University of Michigan team to a top five finish in the highly competitive and extremely challenging Putnam exam. This exam is administered yearly and is open to all college students in North America; usually around 430 universities and colleges send teams to compete in the Putnam. The exam has been offered since the thirties and finishing at the top carries a great deal of prestige. Razvan's superior abilities led to the spectacular success of the Michigan team which was no mean feat.

My own experience with the book has been one of revelation with each passing page. I used the book to teach the problem solving course at the University of Michigan, Ann Arbor, and it helped me immensely. The book possesses a variety of topics in elementary mathematics, ranging from algebra to geometry to trigonometry to number theory. Each chapter is divided into sections and each section has a theme. In keeping with the theme, the authors mention some useful formulae and/or facts that may be used in that section. This is followed by a demonstration of some dazzling problem solving techniques applied to a couple of problems. This is then followed by a list of challenging problems of varying levels of difficulty, all related to the theme of the section. There are roughly 18 such sections and many, many problems to think about. The rest of the book, which is the bulk of it, is dedicated to providing elegant solutions to every problem posed in the first part. Occasionally a problem merits more than one solution and sometimes the way is pointed to some interesting mathematics. The authors also acknowledge the source of many of the problems in the book which is a good indicator of the pedigree of the problem. Almost every solution is a gem and each problem demands its own style of solution. As noted earlier, problem solving is a skill and the authors try and succeed in conveying that idea in the problems and solutions they present.

Here is a sample problem from the book; if you can't do it and want to know how, check out the book:

"Show that any cube can be divided into 'n' cubes for any integer 'n' bigger than 54."

In summary if you are interested in figuring out puzzles, if you are a problem solver of elementary mathematical problems, or if you are just plain curious how a large fraction of mathematicians got hooked on mathematics, I would highly recommend you give this book a try. You may learn something and may even enjoy yourself in the process.

this is an extremely useful book. loaded with lots of efficient, accurate, easy to understand code. this is the most user-friendly book on numerical algorithms that i have found.

The author is clearly very familiar with the theory and practice of numerical computations in OO languages. For me, the main contributions of the book are an expert formulation of some of the basic numerical techniques and concepts in OO terms (a subject rarely approached in the numerous existing books on OO technology), and examples that can be followed to implement other NM techniques and concepts.

The inclusion of very readable Smalltalk and Java source code is very useful.

For use in a course, I would like to see the material complemented by exercises.

I really enjoyed this book because it shows that a high level language such as Smalltalk can efficiently model a complex domain like numerical methods. Besset presents a conceptual framework where the concepts are extended and reused, showing the power of OO programming. I also liked the structure of examples followed by a formal foundation, implementation, and implementation discussion.

This is a very good book on queuing theory and applications. I have read or looked at dozens of similar books, but find this to be one of the best. ...

For quantification of models, queuing theory is one of the oldest variants, used for simple first feelings with the processes. This book provides the theory of random arrive processes, probability and all kind of queues. The reader is introduced in single queues like m/m/1 and learns how to calculate and forecast the most important statistics as a start. Later on more complex queue systems are explained in detail all with graphs, simple formulas to calculate and enough text to understand the formulas and steps of the writer. At the moment the theory of the single queues cannot solve the problem, the writer uses some PASCAL procedures. At that moment it looks more like a black box to me, but I am sure the writer knows what he is doing!

The UK edition of Practical Queueing Analysis is available, and can be ordered from McGraw-Hill in the US (609-426-5793). The only difference between the USA and UK editions is a diskette that was shrink-wrapped with the US edition. Contact Mike Tanner if you need the diskette.

The book investigates the proper mathematical structure of probability and statistics (commonly refered as stochastics).

The author claims that the identification of probability as frequency is too restricted. He proposes its interpretation
as 'logic under uncertainty' where uncertainty means not randomness
but lack of full descriptions and data.

He says the second approach is much more powerfull.

Although I have no problem in accepting the authors
proposal for applications of stochastics (say in the
social sciences) I am definitely
of the opinion that the probabilities of equilibrium statistical
mechanics are objective ie mathematical consequences of Hamiltonian systems theory and that their assignment has
nothing to do with the stochastician's degree of
information.

In non-equilibrium statistical
mechanics the story is much more difficult; there you can not use sampling in space ot time
to define probabilities because the system is not uniform
in space or time. From the stochastics (Kolmogorov
measure theory) point of view, you can
always do a series of evolutions of your dynamical
system weighting your initial conditions with any probability
you choose and subsequently define evolution of probabilities
using the dynamical equations of motion. But when it comes
to experiment the question arises: what are the initial probabilities? The experimentalist can not answer that
in a definite way, cause if he could he would not have
to use probabilities at the first place.
Assigning probabilities in
NESM requires (I am afraid) a resolution along the lines
proposed by the author. If you try to break the deadlock
by using enemble averages for the initial conditions, you simply
translate the problem to the initial conditions of the ensemble you use in order to define the initial conditions of the initial ensemble and so on to infinity. There is no way to assign
probabilities as in ESM because the NESM probabilities
are not a property of the equations of motion, but rather
of the initial conditions.

The serious problem that arises in this way is that since the
equations of motion can only inform about the way the initial
information in been transformed by the evolution of the system,
you never gain any information in addition to what you put in
the initial conditions. So not only your initial assignment
can be partial info, it can also be wrong info. NESM then
is reduced to an expensive way to evolve junk information.

It is honest to say that following ET Jaynes point of view
the central problem in NESM is not the physical equations
of motion *but how much* one knows about the initial state of the system.

I guess we have to live with that...

Finally I agree
completely with the authors view of quantum mechanics as
an incomplete theory.

This book has been on the web in unfinished form for a number of years and has shaped my scientific thinking more than any other book. I believe it constitutes one of the most important scientific texts of the last hundred years. It convincingly shows that "statistics", "statistical inference", "Bayesian inference", "probability theory", "maximum entropy methods" , and "statistical mechanics" are all parts of a large coherent theory that is the unique consistent extension of logic to propositions that have degrees of plausibility attached to them. This is already a theoretical accomplishment of epic proportions. But in addition, the book shows how one actually solves real world problems within this frame work, and in doing so shows what a vastly wider array of problems is addressable within this frame work than in any of the forementioned particular fields.
If you work in any field where on needs to "reason with incomplete information" this book is invaluable.

As others have already mentioned, Jaynes never finished this book. The editor decided to "fill in" the missing parts by putting excercises that, when finished by the reader, provide what (so the editor guesses) Jaynes left out. I find this solution a bit disappointing. The excercises don't take away the impression that holes are left in the text. It would have been better if the editor had written the missing parts and then printed those in different font so as to indicate that these parts were not written by Jaynes. Better still would have been if the editor had invited researchers that are intimately familiar with Jaynes' work and the topic of each of the missing pieces to submit text for the missing pieces. The editor could then have chosen from these to provide a "best guess" for what Jaynes might have written.

Finally, there is the issue of Jaynes' writing style. This is of course largely a matter of taste. I personally like his writing style very much because it is clear, and not as stifly formal as most science texts. However, some readers may find his style too belligerent and polemic.

To "pure" mathematicians, probability theory is measure theory in spaces of measure 1. To the extent to which you remain a "pure" mathematician, this book will be incomprehensible to you.

To frequentist statisticians, probability theory is the study of relative frequencies or of proportions of a population; those are "probabilities".

To Bayesian statisticians, probability theory is the study of degrees of belief. Bayesians may assign probability 1/2 to the proposition that there was life on Mars a billion years ago; frequentists will not do that because they cannot say that there was life on Mars a billion years ago in precisely half of all cases -- there are no such "cases".

To _subjective_ Bayesians, probability theory is about subjective degrees of belief. A subjective degree of belief is merely how sure you happen to be.

"Noninformative" _objective_ Bayesians assign "noninformative" probability distributions when they deal with uncertain propositions or uncertain quantities, and replace them with "informative" distributions only when they update them because of "data". "Data", in this sense, consists of the outcomes of random experiments.

"Informative" _objective_ Bayesians -- a rare species -- ask what degree of belief in an uncertain proposition is logically necessitated by whatever information one has, and they don't necessarily require that information to consist of outcomes of random experiments.

Jaynes is an "informative" objective Bayesian. This book is his defense of that position and his account of how it is to be used.

"Pure" mathematicians will not find that this book resembles that branch of "pure" mathematics that they call probability theory.

Jaynes rails against those he disagrees with at great length. Often he is right. But often he simply misunderstands them. For example, writing in the 1990s, he said that pure mathematicians reject the use of Dirac's delta function and its derivatives, and related topics. That is nonsense; the delta function has long been considered highly respectable, and required material in the graduate curriculum. Unfortunately Jaynes's misunderstandings may cause some others to misunderstand him when he is right. Statisticians are more informed than "pure" mathematicians and will disagree with Jaynes for better reasons. _Some_ statisticians will agree with him.

Jaynes has many flaws, made all the more annoying by the fact that we need to overlook them in order to understand him. His message is important.

This is great book for learning statistics. The best I've seen so far. Statistics is a subject I hate, but this book has given me hope. I know use it as a supplement for other textbooks. It was a book that was written with the student in mind. I am in a class with an awful stats book now, Statistical Methods for Psychology. But, the Statistics for the Behavioral Sciences text will be a permanent fixture on my shelf and I recommend it often. I will continue to look for textbooks written by these guys. Thanks, you made one of my semesters much better.

OK, here it is: I am a math phobic, have been all my life, as long as I can remember myself! So, when I started studying psychology as a second degree, I was kind of anxious about taking all kinds of statistics courses: it seems that statistics are a major part of any psychology degree, & so it was important for me to learn them well, from the beginning. Well, with this book (which I shopped around for, looking for the best introductory book on the subject) my math-phobia has not disappeared, but is slowly & surely getting smaller & smaller. This is a textbook that guides you, step by step, so you can understand all the basic concepts of statistics, without feeling you're making an effort. Lots of problem-solving & learning checks help, lots of revision at the end of each chapter...the book is organized in an excellent & thoughtful way, perfect for a student who will take the time sto study (it covers almost everything) but who wants to do it in an organized way.

I used an older copy of this book as an undergrad and was asked by a fellow doctoral student what I might recommend for use as a good stats book for the "stat phobic" ... Hands down, I say that THIS is the book to use. I've used thinner stats books that pretend to be cute. But if "cute" is not what you need, and you need to learn the stuff as well as reference the stuff. This is the book for you.

I suppose I ought to update my copy ;-) mine is dog eared!

Need stats? Buy this book to learn. Good stuff!

Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book. All the data thats used is available in the authors webbsite for downloading, very nice.

To make this review short, I will say that I agree with all seven points made by the reviewer from New York, NY, whomever he or she may be. Franses is clear, concise, authoritative and up-to-date on all the advances.

I particularly like the nice coverage of GARCH models that are new to me. It is a great introductory text especially for economics majors. For more advanced books and other treatments of time series consider Kennedy's fourth edition of "A Guide to Econometrics" or the suggestion from reviewer "New York, NY". Also my listmania list on time series will give you several sources to look at.

Recently, I reread Franses book and expanded my review, which now includes 10 benefits.
(1) Organization by key features of economic time series (trends, seasonality, outliers, conditional heteroskedasticity, non-linearity), rather than by methods, which provides a practical foundation for the various methodologies. The order in which chapters are presented reflects the order of difficulty in modeling trends, seasonality, etc. Even if there were no other benefits, this organization makes it worthwhile.
(2) Appropriate level for first book on time series models as applied to economic time series, explaining more difficult concepts GARCH and VAR without excess detail. Box and Jenksins book is more a textbook; Brockwell and Davis is also more advanced; Hamilton is comprehensive and technical, but not as friendly. This book is very approachable even if you have had only 1 or 2 statistics courses. In economics, many people are interested in forecasting, and Franeses here is a good start. If you are looking for a more advanced forecasting book, try the recent books by Clements and Hendry from Cambridge U Press.
(3) Clear distinction of the steps of model identification, estimation, diagnostics, and selection; something which other time series analysis books do not seem to do early or easily. (4) Delineates stochastic and deterministic models in the second chapter, providing a framework for when to take differences (eg. ARMA vs ARIMA). His timing is excellent. Many people I have interviewed on time series do not understand why they need to difference (eg use prices instead of returns) or why to transform the series (eg use logs instead of actual values).
(5) Generous use of examples with real not simulated data with a website to download all the data, making it possible to import, graph, and analyze on your own.
(6) A website containing printing corrections. Techincal books are likely to have some errors, but very few keep websites to list what those are.
(7) Revealing graphics, especially for conditional heteroskedasticity, the 'CH' in GARCH. Figures 7.1-7.3 illustrate the concept that large returns tend to follow large returns very cleanly.
(8) His notation is clear and consistent, yet not overwhelming: conventional Greek letters, only 1 level of subscripting, matrix noation where appropriate; even the results are neatly presented, as standard errors appear in () below their point estimates. Finally, Franses uses the same notation from chapter to chapter where the term is the same--not so common when chapters written by different authors.
(9) Great appendices: extensive and updated references, a thorough subject index, and an author index. My only suggestion for improvement is that a second edition or the website should contain some exercises. Highly recommended.
(10) The price! There are books published under Wiley at 3 to 4 times the price! under Springer Verlag for 2 to 3 times the price. Certain books are worth the money, but Cambridge University Press paperback publications, when written well, are exeptional values. I encourage the ambitious time series student to look at other time series books, including one written this year by Franses including Quantitative Models in Market Research.

Hosmer and Lemeshow point to the massive growth in applications of logistic regression over a ten year period from the time of publication of the first edition of their text. They found over 1000 articles that used logistic regression during that time frame. There also have been many software advances that make it easier to apply logistic regression. The authors do their computing mostly in STATA. But they also acquaint the reader with many other useful standard packages for applying logistic regression. They also provide a web site from the publisher where data sets can be found.

New topics include the use of exact methods in logistic regression, logistic models for multinomial, ordinal and multiple response data. Also included is the use of logistic regression in the analysis of complex survey sampling data and for the modeling of matched studies.

The book is intended for a graduate course in logistic regression requiring the student to be familiar with linear regression and contingency tables. Similar in spirit and objectives to the first edition, this text also maintains the clarity of thought and presentation that these authors have a history of providing.

This is an important update to the first edition and is worth having on the bookshelf in any biostatistics library. I have my own personal copy and I think many others would also benefit by having it as a reference.

This is an excellent beginning and intermediate text on logistic regression analysis. Avoids the thorny details, but provides a wealth of references for those who are interested.

Anyone who is serious about doing logistic regression analysis should have this book.

This is a very popular and well written text on logistic regression. The topic is very useful to biostatisticians. Hosmer and Lemeshow have taught some short course out of the text which have been well received. The authors are knowledgeable and thorough. The book is very much oriented toward real applications and does not require advanced mathematics.

Like his other books, Franses provides an nice applied treatment of non-linear time series models that are in this case applicable to finance. It includes extensive coverage of regime switching models. It includes data drawn from several financial markets including Tokyo, London and Frankfurt.

The major distinction of the book from Granger&Terasvirta's earlier work is its focus on financial applications of regime switching (RS) models and the author's separate treatment of RS in returns(means) and volatilities(variances) by putting them in different chapters. Another welcome feature is the availability of accompanying procedures in Gauss downloadable from the author's website. I would have expected a lengthier treatment of Markov RS models but I guess either the authors leave this to Tsay's new book or quote Hamilton as classical reference source.

The major distinction of the book from Granger&Terasvirta's earlier work is its focus on financial applications of regime switching (RS) models and the author's strategy of separate treatment of RS of returns(means) and volatilities(variances) by putting them in different chapters. Another wellcome feature is the availability of accompanying procedures in Gauss downloadable from the author's website. I would have expected a lengthier treatment of Markov RS models but I guess either the authors leave this to Tsay's new book or quote Hamilton as classical reference source.

This book actually can be read by non-math majors.

Watsham really makes the effort needed to make
the book "readable" to non-quants.

Unlike Neftci and Wilmott, who jump to more advanced material
without really explaining most of the details,
Watsham explains all the needed details.

However, Watsham's book covers much fewer topics
than Neftci's or Wilmott's (Quant finance) book covers.

I hope Mr. Watsham next edition includes more of the
topics that are found in Netfci's book.

It is one book that covers a wide range of basic to intermediate level concepts that are essential for any quantitative finance practitioner, financial analyst, portfolio manager and derivative traders. The book assumes some very basic mathematical background. Throughout the book the authors seem to have deliberately relied on basic algebra rather than complex integration and differentiation -- except in some relatively advanced topics that are covered in final few chapters of the book. I highly recommend reading this book to build a solid background of mathematics that is now commonplace in finance.

This book targets readers who have little or no familiarity with statistics and calculus (or who, like me, has forgotten much of these two disciplines. The authors do a great job of explaning why we use the methods they explain. Clear examples are provided. Complex subjects are built up from simpler principles. I highly recommend this bood to students seeking a thorough grounding in the quantitative methods underlying the pricing of assets and derivatives, portfolio management, risk management, etc.

This is a useful book for the working statistician or consultant. Many questions arise in practice that are never covered in traditional textbooks, and with experience an applied statistician learns "rules of thumb". Here is a text that nicely organizes some of the most common questions and problems and design considerations, with solid practical advice. This is not a text for a course (unless a course in consulting), but would serve an applied researcher or statistician well.

Excellent reference for statisticians. Only two complaints:
1) In many instances, wording is not clear - you have to really pick sentences apart to figure out what the author meant.
2) Reasons for a particular rule sometimes leave you wanting. But at least you're introduced to the concept and can look elsewhere for assistance in understanding.

Also, the title might lead some non-statisticans to think that they can pick this book up and learn how to plug and chug in all sorts of situations. This is not the case.

What a great idea. Rather than write a comprehensive text, Dr. VanBelle writes about a large number of statistical topics, focussing on areas that are confusing, or frequently misunderstood. Some of his "rules of thumbs" are approximate formulae for doing quick (but approximate) analyses. Most are more general advice, based on decades of consulting. Topics range from designing studies to making graphs, but most are about data analysis. Something for everyone in a well-written book.