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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

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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

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    Available in PDF Format | 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.pdf | English
    Sharon Bertsch Mcgrayne(Author)
Drawing on primary source material and interviews with statisticians and other scientists, "The Theory That Would Not Die" is the riveting account of how a seemingly simple theorem ignited one of the greatest scientific controversies of all time. Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years - at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information, even breaking Germany's Enigma code during World War II, and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA decoding to Homeland Security. "The Theory That Would Not Die" is a vivid account of the generations-long dispute over one of the greatest breakthroughs in the history of applied mathematics and statistics.

Superb. Andrew Hacker, New York Review of Books--Andrew Hacker "New York Review of Books ""

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  • By David Hitchin on 18 July 2011

    Whether or not you will enjoy this book depends on who you are. If you enjoy reading books about popular science, and trying to solve the occasional simple mathematical or logical puzzle, then you are ready for this one. If you want to understand the theory in any depth, or use it to solve problems, then you will need at least first-year undergraduate statistics to get started, much more to make progress -­ and a book with the formal mathematics, but begin with this one first to get a perspective on the field before going into detail.It is not obvious how you should use data to decide what to believe or how to act, and, as theories of statistics were developed, statisticians tried several different ways of thinking about data and the conclusions that could reasonably be drawn from them. Unfortunately the divisions of opinion (perhaps largely due to the personalities of the leading thinkers) resulted in acrimonious and inconclusive arguments.Thomas Bayes was a clergyman who died in 1761, leaving behind some mathematical papers. One of these was revised and corrected by Richard Price, so we don't know quite what Bayes wrote or what he meant. This paper was the origin of two things: (1) the widely-used and uncontroversial `Bayes Theorem', and (2) the controversial idea that probability could be expressed in terms of a measure of belief. In Bayesian statistics the researcher puts a belief into numerical terms and refines this belief in the light of subsequently observed data. The 'subjective' aspect of the theory brought it into disrepute, where it lingered for nearly 200 years. Many people faced with practical problems found that Bayesian methods worked, but either they didn't know about Bayes or they preferred not to invite criticism by mentioning his name.In the last 60 years or so there has been a big revival in interest in Bayes theory, and it has been used to solve many problems that weren't amenable to traditional methods. The big barrier was that some of the methods needed huge calculations, but with the availability of cheap, fast computers and new methods of calculation that barrier has almost disappeared.Sharon Bertsch Mcgrayne's book gives a very clear and thorough history of "the theory that would not die." As a practising statistician for more than 40 years I knew much of the published work that she has written about, and can vouch for her accuracy (there are a few corrections on her website), but until I read this book I did not have a clear idea of all of the historical developments and controversies. My only criticism is that the bibliography is organised by chapters, rather than as one alphabetically ordered sequence.

  • By Conall Boyle on 23 June 2013

    Well first off, I'm delighted to see that co-founder Richard Price of Llangeinor is given proper credit. (Llangeinor in South Wales, is near where I live, But Rev Price did much more than re-write Rev Bayes's notes)And I'm fascinated by the names of all the statisticians who I'd heard about, and a few I've even met (I taught stats at a midlands University).But having re-read it more closely, I now understand my quibbles: All Bayesians are treated as unsung heroes, the un-converted are knaves.For instance: p116 "Cornfield's identification [in the Framingham study] in 1962 of the most critical risks factors [high cholesterol, high blood pressure] for cardiovascular disease produced....a dramatic drop in death rates from c.v. diease.", because it seems that Cornfield used Bayes and the others didn't.Now this is a complete travesty! Read Gary Taubes 'The Diet Delusion' and you'll discover that poor analysis, and especially pre-conceptions meant that Framingham produced the 'wrong' results. Apart from smoking, none of the other factors matter. The low-fat obsession is making matters worse. A clear example of bad priors causing wrong posteriors?So did Cornfield and his bayesianism lead to these false conclusions? Ms. McGrayne, the author could be forgiven for not knowing this, but it shows how the book works -- run with any 'success' for bayesianism (and ignore the failures?)Her attitude to my favourite statistician, Tukey is bizarre to say the least. She claims he did all sorts of secret work both for the military and for commercial clients that used Bayes, yet ignored his plain-sight comments that EDA -- exploratory data analysis was what matters to most problem solvers; that CBA confirmatory data analysis was just an ornamental final flourish, and that was true for both bayesians and frequentists.[disclaimer: I wrote a book on EDA misleadingly titled 'Mastering statistics with your micro-computer' 1986]p 236 is to say the least, disingenuous! Greenspan, chairman of the Fed said in 2004 he used bayesian ideas to assess risk in financial policy. Ooops! He was proven spectacularly wrong by 2008! But Greenspan, claims Ms McGrayne didn't do Bayes properly. ho! ho! pull the other one!This is a good book, well researched, and shines a light on otherwise neglected characters (statisticians, like me!). But she's caught the bayesian bug in spades!

  • By David on 11 December 2012

    I have to agree with the other reviewers who were disappointed by the lack of mathematics in this book. To borrow an old cliche, Bayes without the mathematics is Hamlet without the prince. It is certainly interesting to read about the academic squabbles, the logical breakthroughs, the military applications, and so on; but I want to know HOW (for instance) Turing used Bayes to decode Enigma, not merely THAT he used Bayes. I wonder just how many readers would pick up the book if they didn't already have some understanding of what Bayes was about; but if McGrayne were worried about the ability of her readers to follow a mathematical explanation then all she needed to do was relegate the detailed explanations to appendices. She deserves credit for the appendix on mammograms and breast cancer, which is admirably simple, but as far as I can see that is the only point at which even the algebraic statement of the familiar theorem appears.I first came across the Bayesian approach to statistics as a graduate student in 1970 (thanks to Tribus' "Rational Descriptions, Decisions and Designs" - pity he didn't get a name check from McGrayne) and, like Saul on the road to Damascus, I underwent something like a religious conversion. Unlike St Paul, I never suffered any persecution in consequence, but it is good to see that what seemed to me at the time a fringe religion has now achieved something approaching statistical orthodoxy. For that reassurance, I thank Ms McGrayne.

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