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The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy Hardcover – May 17, 2011

3.7 out of 5 stars 126 customer reviews

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Editorial Reviews


"If you're not thinking like a Bayesian, perhaps you should be."—John Allen Paulos, New York Times Book Review
(John Allen Paulos New York Times Book Review)

"A masterfully researched tale of human struggle and accomplishment . . . . Renders perplexing mathematical debates digestible and vivid for even the most lay of audiences."—Michael Washburn, Boston Globe
(Michael Washburn Boston Globe)

“Well known in statistical circles, Bayes’s Theorem was first given in a posthumous paper by the English clergyman Thomas Bayes in the mid-eighteenth century. McGrayne provides a fascinating account of the modern use of this result in matters as diverse as cryptography, assurance, the investigation of the connection between smoking and cancer, RAND, the identification of the author of certain papers in The Federalist, election forecasting and the search for a missing H-bomb. The general reader will enjoy her easy style and the way in which she has successfully illustrated the use of a result of prime importance in scientific work.”— Andrew I. Dale, author of A History of Inverse Probability From Thomas Bayes to Karl Pearson and Most Honorable Remembrance: The Life and Work of Thomas Bayes

(Andrew I. Dale 2010-08-19)

“Compelling, fast-paced reading full of lively characters and anecdotes. . . .A great story.” —Robert E. Kass, Carnegie Mellon University

(Robert E. Kass)

"Makes the theory come alive. . .enjoyable. . .densely packed and engaging, . . .very accessible. . .an admirable job of giving a voice to the scores of famous and non-famous people and data who contributed, for good or for worse."—Significance Magazine
(Significance Magazine)

"A very compelling documented account. . .very interesting reading."—Jose Bernardo, Valencia List Blog
(Jose Bernardo Valencia List Blog)

"To have crafted a page-turner out of the history of statistics is an impressive feat. If only lectures at university had been this racy."—New Scientist
(New Scientist)

The Theory That Would Not Die is an impressively researched, rollicking tale of the triumph of a powerful mathematical tool.”—Andrew Robinson, Nature Vol. 475
(Andrew Robinson Nature Vol. 475 2011-07-28)

"A lively, engaging historical account...McGrayne describes actuarial, business, and military uses of the Bayesian approach, including its application to settle the disputed authorship of 12 of the Federalist Papers, and its use to connect cigarette smoking and lung cancer...All of this is accomplished through compelling, fast-moving prose...The reader cannot help but enjoy learning about some of the more gossipy episodes and outsized personalities."—Choice

“McGrayne is such a good writer that she makes this obscure battle gripping for the general reader.”—Engineering and Technology Magazine
(Engineering and Technology Magazine)

"McGrayne explains [it] beautifully...Top holiday reading."—The Australian
(The Australian)

"Engaging....Readers will be amazed at the impact that Bayes' rule has had in diverse fields, as well as by its rejection by too many statisticians....I was brought up, statistically speaking, as what is called a frequentist...But reading McGrayne's book has made me determined to try, once again, to master the intricacies of Bayesian statisics. I am confident that other readers will feel the same."—The Lancet
(The Lancet)

"Thorough research of the subject matter coupled with flowing prose, an impressive set of interviews with Bayesian statisticians, and an extremely engaging style in telling the personal stories of the few nonconformist heroes of the Bayesian school."—Sam Behseta, Chance
(Sam Behseta Chance)

"A fascinating and engaging tale."—Mathematical Association of America Reviews
(Mathematical Association of America Reviews)

"For the student who is being exposed to Bayesian statistics for the first time, McGrayne's book provides a wealth of illustrations to whet his or her appetite for more. It will broaden and deepen the field of reference of the more expert statistician, and the general reader will find an understandable, well-written, and fascinating account of a scientific field of great importance today."—Andrew I. Dale, Notices of the American Mathematical Society
(Andrew I. Dale Notices of the American Mathematical Society)

"A very engaging book that statisticians, probabilists, and history buffs in the mathematical sciences should enjoy."—David Agard, CryptologIA
(David Agard CryptologIA)

"Delightful ... [and] McGrayne gives a superb synopsis of the fundamental development of probability and statistics by Laplace."—Scott L. Zeger of Johns Hopkins, Physics Today 
(Physics Today Scott L. Zeger)

“Superb.”—Andrew Hacker, New York Review of Books 
(Andrew Hacker New York Review of Books)

About the Author

Sharon Bertsch McGrayne is the author of numerous books, including Nobel Prize Women in Science: Their Lives, Struggles, and Momentous Discoveries and Prometheans in the Lab: Chemistry and the Making of the Modern World. She lives in Seattle.


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Product Details

  • Hardcover: 336 pages
  • Publisher: Yale University Press; 56125th edition (May 17, 2011)
  • Language: English
  • ISBN-10: 0300169698
  • ISBN-13: 978-0300169690
  • Product Dimensions: 6.1 x 1.1 x 9.2 inches
  • Shipping Weight: 1.4 pounds
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (126 customer reviews)
  • Amazon Best Sellers Rank: #793,609 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Kindle Edition Verified Purchase
"The Theory That Would Not Die" is an enjoyable account of the history of Bayesian statistics from Thomas Bayes's first idea to the ultimate (near-)triumph of Bayesian methods in modern statistics. As a statistically-oriented researcher and avowed Bayesian myself, I found that the book fills in details about the personalities, battles, and tempestuous history of the concepts.

If you are generally familiar with the concept of Bayes' rule and the fundamental technical debate with frequentist theory, then I can wholeheartedly recommend the book because it will deepen your understanding of the history. The main limitation occurs if you are *not* familiar with the statistical side of the debate but are a general popular science reader: the book refers obliquely to the fundamental problems but does not delve into enough technical depth to communicate the central elements of the debate.

I think McGrayne should have used a chapter very early in the book to illustrate the technical difference between the two theories -- not in terms of mathematics or detailed equations, but in terms of a practical question that would show how the Bayesian approach can answer questions that traditional statistics cannot. In many cases in McGrayne's book, we find assertions that the Bayesian methods yielded better answers in one situation or another, but the underlying intuition about *why* or *how* is missing. The Bayesian literature is full of such examples that could be easily explained.

A good example occurs on p. 1 of ET Jaynes's Probability Theory: I observe someone climbing out a window in the middle of the night carrying a bag over the shoulder and running away. Question: is it likely that this person is a burgler?
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Format: Hardcover Verified Purchase
This book moves through the history (so far) of the development and application of Bayes rule. It is a good story, and the book is well written. Unfortunately, it is somewhat mixed in the manner material is presented. For example, the author provides significant detail on the application of the rule to activities such as code cracking and finding submarines but she then goes on to list a large number of more recent applications with very little historical background. Maintaining consistency of depth for each application discussed would have significantly improved the "story". I would recommend this book to anyone who is interested in the history of science, statistics and mathematics, but be prepared for a "patchy" read.
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Format: Hardcover Verified Purchase
Sharon Bertsch Mcgrayne is a talented science writer whose portraits of great scientists of the past are incisive and entertaining. However, she evidently believes that one must studiously avoid dealing with any serious scientific issues in entertaining a popular audience. For this reason, this book is a total failure. Why should a reader care about the history of an idea of which he or she has zero understanding? Mcgrayne turns the history of Bayes rule into a pitched battle between intransigent opponents, but we never find out what the real issue are.

In fact, Bayes rule is a mathematical tautology, being the definition of conditional probability. Suppose A is an event with probability P(A) and B is an event with probability P(B). Let C be the event "both A and B occur." Then the conditional probability P(A|B) of event A, given that we know that B has occurred, just P(C)/P(B). Moreover, if a decision-maker knows P(A), P(B), and P(C), and discovers that B occurred, then he should revise the probability that A occurred to P(A|B) = P(C)/P(B). Why? Well, suppose we have a population of 1000 individuals, where the probability that an event E is true of an individual is P(E), where E is any one of A, B, and C. Then the expected number of individuals for which B is true is 1000*P(B). Of these, the number for which A is also true is 1000*P(A). Therefore, the probability that an individual satisfies A, given that he satisfies B, is 1000*P(A)/1000*P(B) = P(A|B).

For instance, suppose 5% of the population uses drugs, and there is a drug test that is correct 95% of the time: it tests positive on a drug user 95% of the time, and it tests negative on a drug nonuser 95% of the time.
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Format: Hardcover Verified Purchase
While I completely agree with those reviewers who expressed disappointment with the lack of technical depth in the author's presentation, what I find even more disturbing is her abuse of reference material. References appear in two sections: in footnotes to each chapter, and then, again, as a bibliography with separate sections for each of the book chapters. This leads to some (expected and excusable) duplication, but there are indefensible gaps, in which footnotes may refer to books or articles that cannot be found in any of the bibliographies, let alone in those dedicated to the footnoted chapter. It is even more unfortunate that many of the references given do not permit the interested reader to fill in the technical and scientific detail of the applications the author so temptingly describes, or, in the case of the missing H-bomb, seems to describe. One is driven to the conclusion that almost none of the text pertains to actual application of Bayes' Rule, or, indeed, to any sort of statistical analysis.

It is quite true that the historical presentation is replete with biographical anecdotes, and they are a joy to those of us interested in the history of statistics (and philosophy and logic) - but, alas, that makes even more frustrating the utter absence of technical, mathematical detail.

Though much of the book purports to be a presentation of the Bayesian vs. Frequentist controversy, it deals with the latter no more deeply or fruitfully than the former. In brief, it is truly a "hands-off" presentation, hence immensely disappointing. If only it were the book the jacket blurbs describe!
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