The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of C by Sharon Bertsch McGrayne (PDF)

Description

“This account of how a once reviled theory, Baye’s rule, came to underpin modern life is both approachable and engrossing” (Sunday Times). A New York Times Book Review Editors’ Choice 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 generations-long human drama surrounding it. McGrayne traces the rule’s discovery by an 18th century amateur mathematician through its development by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—while practitioners relied on it to solve crises involving great uncertainty and scanty information, such as Alan Turing’s work breaking Germany’s Enigma code during World War II. McGrayne also explains how the advent of computer technology in the 1980s proved to be a game-changer. Today, Bayes’ rule is used everywhere from DNA de-coding to Homeland Security. 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 controversies of all time.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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.

⭐Wow, what an interesting book! I thought I would read it because I wasn’t too clear on what I learned about Bayes Rule in statistics class . I expected it to be rather dry and boring. I was wrong. It read like a novel. If you have any interest in science and discovery there is a high probability you will enjoy this book.

⭐I only understood a small fraction of the underlying issues, but the intellectual history here is fascinating. The personalities, the triumphs, the applications, all of it. Fascinating.

⭐The history is well researched. The book produces a list of topics and their relationships that is very useful in producing an improved strategy for the study of machine learning, neuroscience, statistics, finance, and investing.

⭐The story of Bayesian theory is interesting as it delves into many areas. Some readers may find it somewhat tedious but if you have any interest in Bayes theory and applications it is well worth the time.

⭐The author details the history of statistics and their use in solving many of the problems facing governments, military, health sciences and many other cases.I enjoyed reading about the ongoing battle between Bayesians and frequentists. The examples provided by the author were helpful in clarifying how each approach worked.Sometimes dry, but mostly a fast read, as the author always brings you full circle through the applications of each theorem and how successfully or not they wre applied.

⭐Nice to finally learn about the names I’ve heard in stats classes over the years (Tuket, Neyman etc.) Good read, well-written, will appeal to those curious about intellectual history and the development of statistics.

⭐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!

⭐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.

⭐This book seems to be not much more than an a promotional tract for Bayes . It depicts a series of situations where Bayes has been found useful, but nothing more than that. Do not expect anything which will help a beginner or anyone else to advance her or his understanding of the theorem or, or for the most part, even the simplest guidance as to how it might be applied in a practical situation.

⭐This riveting read deserves five stars because there are lots of books, papers, and websites about the maths of Bayes but this is the first really good description of the history and personalities involved. I think it’s great.If you’re interested in the maths it is perhaps best to look for original papers on the internet. I do know enough of the maths that I’m not frustrated, and I understand enough of the principles to know that the author has grasped them pretty well.I have books whose authors were just names to me before but now they are personalities. I have also been able to put things into a useful historical context and see how the events described in the book have an influence even now.Some of them actually happened within my lifetime and remind me of the intolerant git who presented the course on statistics within my psychology degree course.

⭐Very well written book – well structured, entertaining and researched. A useful introduction to Bayes theorem and application.But it’s just a little too much about the history of classic statistics vs pragmatic (Bayes) practicioners. And some of the history is very recent – the disagreements over the different approaches still going into the 1990s – though I do now feel the need to double check this schism against other sources.Rather too little on the mathematics. The basic concept is easy and well covered enough in the Appendix giving some worked examples, but there is clearly much more to it that that eg MCMC. At some point I’ll want to have a play with WinBUGS.

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