Well written, this book goes through elementary probability and then hits all of the great topics of using the computer to generate and check random numbers!

Sornette's book is quite an achievement both in quantity and
quality. The presentation remains informal and quite readable; it reads like a physics textbook, not a math textbook. The references are very extensive (a total of 832! altogether) and they are a very valuable component of the book. In fact much of the book is about the reference material. You might choose to read the book instead of the 832 references... I think this is
the point...

There's probably nothing wrong with this book besides the fact that it throws it all at you at a high degree of sophistication and in as terse a way as possible, it seems. It's a unique and beautiful achievement but because it is so dense with information and insight, it seems every word counts for ten and you'll want to read several chapters again and again. Also, even though there is a clear unifying theme from chapter to chapter, the book simply ends almost like in the middle of a sentence. After machinegunning out 392 pages of material at research level spanning quite a few scientific fields, there is absolutely no attempt to put it all together. It's up to you to do it and it almost seems like the author is indirectly suggesting you start reading it all over again to "get it"... So, for the second edition, perhaps the author will be bold and add ten pages of wrap up material at the end so that this will read less like an atlas. Apart from that, it's the best!

A much needed book for the biologist interested in using DNA/protein microarrays. Examples are specific for microarrays. The material starts from ground zero and begins
with image analysis. All major methods for analysis are discussed.
Well worth the cost, quality graphics, includes software (have not used as yet).
A must read before discussing experimetnal design with your stats person.

The new book,DEGREES OF BELIEF. is much more than the subtitle, Subjective Probability and Engineering Judgment, would imply. The author presents a scholarly, sensible treatise that covers both theory and practice and, most importantly, builds a beautiful bridge between them.

The various quotes from lectures and writings of Karl Terzaghi, Ralph Peck and other giants of the engineering profession add much to the book. Moreover, the author's emphasis on the wholeness of theory and practice and that regardless of the paradigm, judgment is imperative,make this book so outstanding.

Two English statisticians have compiled this list of terms, abbreviations, famous probabilists and statisticians that is accessible to experts, beginners and practitioners in all fields that use statistical methods and probability. It is very current and competitive with other dictionaries on this topic including the one that Cambidge University Press puts out. A nice feature of the handy paperback book is its low price. Published in 2002 it is the most current dictionary of its type and includes terms from computational statistics and operations research. So you will find terms like simplex methods and figures from operations research like George Dantzig in addition to famous statisticians such as R. A. Fisher and probabilists like Markov and Kolmogorov. It also contains many names of statistical societies, software packages and professional journals and their acronyms.

The original may be a little dated but this book besides giving you a full dictionary of statistics terms- ideal for students beginning studying statistics- it also gves a guide with the most used and usefull formulas. Well written and with the tables you always need. Ideal for teh first year student but a usefull helper for those who are finishing their studies.

The parts of this book I've read have been clear and accessible for someone with an undergraduate degree in mathematics and some knowledge of stochastic processes. It doesn't needlessly multiply the jargon like some books, and it focuses mainly on the one-dimensional case so that the intuition isn't constantly obscured by matrix notation. Many subjects also have chatty introductions that offer intuition and a bit of relief from the hard work involved in learning this subject.

This book is very usefull for me. Especcially good review about spectral windowing and some programms has some errors (but my meens). Thank Your.

This book is the first well-organized treatise in contingency table analysis using log-linear models. Today it remains the best in the subject. It is extremely fun to read. Tons of examples clearly explained with the help of lots of graphs and tables. Mathematical notations and arguments are clean and elegant. The MIT Press also deserved high praise for making this book with top quality and affordable price to students. In a perfect world, all books in mathematics and statistics should be written and published this way. This book is a gem.

Geoff McLachlan has written a thorough and up-to-date text on discriminant analysis and pattern recognition. There are a number of fine books on discriminant analysis. McLachlan's is one of the best. It is at an intermediate level and provides many of the recent advances including regularized discriminant analysis.

Discriminant analysis and pattern recognition are very similar topics. The term discriminant analysis is common in the statistical literature while pattern recognition is more common in the electrical engineering literature. McLachlan is scholarly and familiar with the literature in both disciplines (not common). He includes over 1200 references with many references from the late 1980s.

Professor McLachlan has been a key contributor to the literature on error rate estimation in discriminant analysis and devotes a great deal of coverage to this important topic. He also includes recent developments on bootstrap methods and summarizes the literature on bootstrap methods for adjusting bias in error rate estimation.

Much of the bootstrap work on error rate estimation involves comparative simulation studies, particular when training sample sizes are small. McLachlan provides a nice summary of the his work, the work of Efron and he also includes discussion of a couple of my simulation studies co-authored with Murthy and Nealy.