- Hardcover: 753 pages
- Publisher: Cambridge University Press; 1 edition (June 9, 2003)
- Language: English
- ISBN-10: 0521592712
- ISBN-13: 978-0521592710
- Product Dimensions: 6.8 x 1.7 x 9.7 inches
- Shipping Weight: 3.5 pounds (View shipping rates and policies)
- Average Customer Review: 4.9 out of 5 stars See all reviews (40 customer reviews)
- Amazon Best Sellers Rank: #161,964 in Books (See Top 100 in Books)
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Probability Theory: The Logic of Science 1st Edition
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Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.
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Top Customer Reviews
If you deal at all with probability theory, statistics, data analysis, pattern recognition, automated diagnosis -- in short, any form of reasoning from inconclusive or uncertain information -- you need to read this book. It will give you new perspectives on these problems.
The downside to the book is that Jaynes died before he had a chance to finish it, and the editor, although capable and qualified to fill in the missing pieces, was understandably unwilling to inject himself into Jaynes's book. One result is that the quality of exposition suffers in some of the later chapters; furthermore, the author is not in a position to issue errata to correct various minor errors. Volunteer efforts are underway to remedy these problems -- those who buy the book may want to visit the "Unofficial Errata and Commentary" website for it, or check out the etjaynesstudy mailing list at Yahoo groups.
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.
Jaynes' knowledge of the history and philosophy of statistics is far deeper than that of most statisticians (including myself). His trenchant style gives the book a narrative drive and cover-to-cover readability that, in my experience, is unique in the field. One such strand is the continual battle between his respect for RA Fisher's abilities, and his exasperation at how wrongheadedly he feels they were channelled. And he doesn't hesitate to take on philosophical heavyweights such as Hume in defending the possibility - - in fact, the necessity - - of inductive inference. However, this style also produces some more bitter fruit, such as the way the author repeatedly likens himself to historical victims of religious persecution.
The book weakens when it turns to applications. Regression with errors in both variables is said to be 'the most common problem of inference faced by experimental scientists' who have 'searched the statistical literature in vain for help on this'. Good points. So why don't the author and editor give us at least a reference for just one of the 'correct solutions' which 'adapt effortlessly' to scientists' needs? And Jaynes' argument that the null hypothesis procedure 'saws off its own limb' would also rule out mathematical proof by reductio ad absurdum.
When estimating periodicities, we're told that 'the eyeball is a more reliable indicator of an effect than an orthodox equal-tails test'. So why not show us the data of the example used, to let us use our eyes? In fact, there's only one graph of empirical data in all the book's 600+ pages.
Several convincing arguments are presented for the use of the Jeffreys (reciprocal) prior for scale parameters, including scale independence. However, just when I was ready to go and use it, there's a warning against the use of improper priors except as 'as a well-defined limit of a sequence of proper priors'. A few pages later a uniform prior is used for the mean of a Gaussian, with no such justification as a limit, which makes it far from clear what exactly is being recommended.
I could give a lot more space to the book's many other insights, and several other annoyances. Instead, I'll finish now by recommending it to anyone interested in the foundations and practice of statistical analysis.