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Review

"If you're not thinking like a Bayesian, perhaps you should be."—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"[An] engrossing study. . . . Her book is a compelling and entertaining fusion of history, theory and biography."—Ian Critchley, Sunday Times"An intellectual romp touching on, among other topics, military ingenuity, the origins of modern epidemiology, and the theological foundation of modern mathematics."—Michael Washburn, Boston Globe"This account of how a once reviled theory, Baye’s rule, came to underpin modern life is both approachable and engrossing."—Sunday Times"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"A very compelling documented account . . . very interesting reading."—José Bernardo, Valencia List Blog"McGrayne explains [it] beautifully. . . . Top holiday reading."—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 statistics. I am confident that other readers will feel the same."—The Lancet"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"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"A fascinating and engaging tale."—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 "A very engaging book that statisticians, probabilists, and history buffs in the mathematical sciences should enjoy."—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 TodayEditor's Choice, New York Times Book Review"We now know how to think rationally about our uncertain world. This book describes in vivid prose, accessible to the lay person, the development of Bayes' rule over more than two hundred years from an idea to its widespread acceptance in practice."—Dennis Lindley, University College London"A book simply highlighting the astonishing 200 year controversy over Bayesian analysis would have been highly welcome. This book does so much more, however, uncovering the almost secret role of Bayesian analysis in a stunning series of the most important developments of the twentieth century. What a revelation and what a delightful read!"—James Berger, Arts & Sciences Professor of Statistics, Duke University, and member, National Academy of Sciences"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"Compelling, fast-paced reading full of lively characters and anecdotes . . . A great story."—Robert E. Kass, Carnegie Mellon University"Fascinating . . . I truly admire [McGrayne’s] style of writing, and . . . ability to turn complex mathematical ideas into intriguing stories, centered around real people."—Judea Pearl, winner of the 2012 Turing Award

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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

Paperback: 360 pages

Publisher: Yale University Press; 37310th edition (September 25, 2012)

Language: English

ISBN-10: 9780300188226

ISBN-13: 978-0300188226

ASIN: 0300188226

Product Dimensions:

6.1 x 0.9 x 9.1 inches

Shipping Weight: 14.4 ounces (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

148 customer reviews

Amazon Best Sellers Rank:

#85,127 in Books (See Top 100 in Books)

"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? A traditional statistical analysis can give no answer, because no hypothesis can be rejected with observation of only one case. A Bayesian analysis, however, can use prior information (e.g., the prior knowledge that people rarely climb out wndows in the middle of the night) to yield both a technically correct answer and one that obviously is in better, common-sense alignment with the kinds of judgments we all make.If the present book included a bit more detail to show exactly how this occurs and why the difference arises, I think it would be substantially more powerful for a general audience.In conclusion: a good and entertaining book, although if you know nothing about the underlying debate, it may leave you wishing for more detail and concrete examples. If you already understand the technical side in some depth and can fill in the missing detail, then it will be purely enjoyable and you will learn much about the back history of the competing approaches to statistics.

This book blew my mind. Most of the examples used and the mathematicians involved I was familiar with, but not the Bayesian angle. I feel like the wool was pulled away from my eyes after reading this book. Other reviews complain about the lack of math in the main text of the book, but I disagree. One I think anyone reading this book knows Bayes Rule and two I think the actual math would get in the way of the story. One of the biggest themes of the book is that Bayes is about practical problem solving and that once computers arrived on scene to allow for the iterative brute force solving that it really took off. The process or way of thinking is made clear in the text. Really it was great read, I found myself texting people while reading saying did you know this was Bayes, over and over.

The historical anecdotes in the book might be interesting to people who use/read statistical analyses in their work as I do. However, don't read this book if you expect to learn how to actually apply Baye's rule or conditional probability. I might have given this book 3 stars but there is a terrible error in the appendix explaining how to apply Baye's rule to breast cancer test results. THE ONLY CONCRETE EXAMPLE IN THE BOOK IS WRONG! What a disappointment. And for those of you who are confused by the results in the appendix: P(B|A) = 32/40 not 32/10000.

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 is a non-technical book that deals with the Baysian Statistics.Thomas Bayes (1701–1761) was a Scottish clergyman who developed the technique. Basically, Bayesian statistics is a set of mathematical formulas where “one's inferences about parameters or hypotheses are updated as evidence accumulates.” Simply put, Bayes allows for our subjective inferences as the starting point of inquiry. Then, with accumulated evidence through testing, those initial assumptions are refined.This sounds a great deal like our common sense approach to life, and it is. We all make hunches about probable outcomes of future events based on incomplete current information, and then change and alter our assumptions based on the results.This book walks a fine line between a technical exposition of Bayesian statistics and a popular one. It does this to the point where I think many readers will feel like they are missing something --- as if the surface is only being skimmed. But the author had no choice; otherwise, the book would have gotten bogged down in technical details most readers can’t understand. So, this book has a fair balance between the two… if not somewhat thin in math while being thick in history!

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