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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
 
 

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 [Kindle Edition]

Sharon Bertsch McGrayne
3.7 out of 5 stars  See all reviews (25 customer reviews)

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"Superb.#160;"New York Review of Books" --Andrew Hacker "New York Review of Books "

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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 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.

Product details

  • Format: Kindle Edition
  • File Size: 774 KB
  • Print Length: 335 pages
  • Page Numbers Source ISBN: 0300169698
  • Publisher: Yale University Press (17 May 2011)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B0050QB3EQ
  • Text-to-Speech: Enabled
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  • Average Customer Review: 3.7 out of 5 stars  See all reviews (25 customer reviews)
  • Amazon Bestsellers Rank: #123,897 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

Most Helpful Customer Reviews
45 of 45 people found the following review helpful
Format:Hardcover|Verified Purchase
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.
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11 of 11 people found the following review helpful
By Paul Bowes TOP 500 REVIEWER
Format:Hardcover
This is an excellent history of the development and application of Bayes' Theorem. Intended for the general reader with an interest in probability and the history of science, it is clearly written with a minimum of mathematics, and covers the ground efficiently.

It is particularly interesting for what it reveals of the way in which new ideas become part of intellectual discourse; in this case, by enduring a long period of suspicion and neglect before being rescued by the enthusiasm of practitioners rather than theorists. McGrayne offers many sidelights on the clandestine uses made of Bayes by the military and the intelligence community, which go some way to explaining why the power of these techniques was so long in receiving acknowledgement. The powerful personalities of the people involved receive extensive attention: no reader will come away from this book in ignorance of the degree to which accidents of institutional history and personal character condition the intellectual environment. Recommended.
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19 of 20 people found the following review helpful
3.0 out of 5 stars Lots of social history, but very little maths 19 Jan 2012
By egmont
Format:Hardcover
This is half a book and the half is very good - it would be worth 5 stars. You learn about the fascinating people who deployed Bayesian inference, particularly the Enigma codebreakers; about the statisticians who thought it was a complete waste of time; about the quirks of history which made people so slow to recognize its value.

All very good. But this is a book about some mathematics, and there is very little maths! Bayes' rule gets an equation, but that's not actually Bayesian inference. The author keeps saying that sometimes frequentists and Bayesians get the same results, but no example. And sometimes very different results, but no example. Bayes himself seems to have proved it, but no details on the proof. Some other people seem to have proved it, but ditto. Bayesian calculations are said to be very difficult pre-computer and pre-MCMC, but no example so you can see why it's such a problem.

So: a little disappointing - but maybe it does provide the questions you can type into Google after this book has not provided the answers.
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7 of 7 people found the following review helpful
By David
Format:Paperback|Verified Purchase
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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Most Recent Customer Reviews
3.0 out of 5 stars Not quite the book I was looking for.
I've never had quite enough use for statistics to justify making a formal effort to learn how to tell what approach is useful for which problem, so once we get beyond the bell... Read more
Published 3 months ago by Ferren
5.0 out of 5 stars a book about people behind a theory
For those who want to learn how statisticians think this book is essential, although the information delivered by this book has very little in common with regular statistics... Read more
Published 4 months ago by mariandragoi
2.0 out of 5 stars Not what I expected
If you like reading about history then maybe the long winded texts are for you. I, on the other hand, hated the book even though I'm pro Bayesian. Sorry!
Published 5 months ago by Steve Forth
5.0 out of 5 stars First class account of a revolution
There appears to be a divergence in views of this book, but I have to say I am fully in the camp of those who consider it an excellent account. Read more
Published 6 months ago by Charles Brewer
5.0 out of 5 stars Brilliant writing makes a specialist subject accessible and...
This book is such a good read! The writer is not only an expert in the subject, but an expert communicator. Read more
Published 6 months ago by H. R. Williamson
5.0 out of 5 stars Very well written...
A very balanced text on the Bayes theorem. Interestingly this book does not pitch this as the wonder theorem that will solve all the problems of the world, but offers a practical... Read more
Published 9 months ago by Amazon Customer
2.0 out of 5 stars Disappointing
As another reviewer wrote, this is about the people who worked on/with the theory, with no explication or examples of the theory itself in use; lots of examples of where it was... Read more
Published 12 months ago by D. E. Stevens
3.0 out of 5 stars Oh Dear! More unbounded enthusaism for Bayes
Well first off, I'm delighted to see that co-founder Richard Price of Llangeinor is given proper credit. Read more
Published 13 months ago by C. F. Boyle
3.0 out of 5 stars Too little simple Maths
Not enough worked examples, even flavours of simple examples might have helped as in Ian Stewarts books.
Anecdotes are interesting.
Published 14 months ago by observer
2.0 out of 5 stars Boring
It seems more a history of statistics and the people around, their behaviour, if gay or not, and sometimes you hear about Bayes
Theorem
Published 14 months ago by Amazon Customer
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Popular Highlights

 (What's this?)
&quote;
by updating our initial belief about something with objective new information, we get a new and improved belief. &quote;
Highlighted by 65 Kindle users
&quote;
the probability of a cause (given an event) is proportional to the probability of the event (given its cause). &quote;
Highlighted by 64 Kindle users
&quote;
At its heart, Bayes runs counter to the deeply held conviction that modern science requires objectivity and precision. Bayes is a measure of belief. And it says that we can learn even from missing and inadequate data, from approximations, and from ignorance. &quote;
Highlighted by 63 Kindle users

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