
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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Narrado por:
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Laural Merlington
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 and listeners, 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.
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.
©2011 Sharon Bertsch McGrayne (P)2012 TantorListeners also enjoyed...




















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Very informative and captivating
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The Story Behind Bayes Theorem (Rule)
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Thorough history
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I had no trouble understanding the narrator, but this is the first audiobook I've listened to in which some proper names (especially French names) were horribly mispronounced.
A fascinating story
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interesting
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I am inspired to learn more about Bayesian and Pascal’s work
Amazing story
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I don't get the bad reviews...
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Unless you’re already completely familiar with the math, I recommend beginning at the end – with the case study in Appendix B (Chapter 24 in the audiobook). According to the author’s numbers, if a woman in her 40s has breast cancer, the chance that she will get a positive mammogram result is about 80%. Nevertheless, if a woman in her 40s gets a positive mammogram result, the chance that she actually has breast cancer is only 3%. Why are the numbers so different? Bayes’s theorem explains how to solve problems like this. In Appendix B, the author works through the application of the theorem to this specific situation, in a way that greatly illuminates the theorem. It makes the rest of the book somewhat more comprehensible.
A nontechnical math history
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educational, curious
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interesting history
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