## Explore Further

### Subject Headings

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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"-- Provided by publisher.

Publisher:
New Haven [Conn.] : Yale University Press, 2011.

ISBN:
9780300175097

Characteristics:
1 online resource (xiii, 320 pages)

Additional Contributors:

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## Comment

Add a CommentI recommend it. Interestingly, McGrayne does not give even a simple little example of how the theorem works.

An excellent account of the history of Bayesian analysis. As one who has lived through the almost religious wars between frequentists and Bayesians I found it not only quite detailed, but very readable as well. It could have used a bit more math to better illustrate some of the basic concepts.

This book is an excellent introduction to Baytex's Rule. The author uses excellent examples in the book to make her points. The only prerequisite for reading the book is an interest in understanding Bayes' Theorem.

A good chunk of this book centers around "frequentist" v. "bayesian" battles in statistics. It's clear that there were personal and emotional components to this battle, but did the frequentists think bayesian math was wrong? It's not very clear. So this book's avoidance of math seems like a mistake--I've seen other books, like Derbyshire's Prime Obsession (http://en.wikipedia.org/wiki/Prime_Obsession), that handle this better.

When the context of the debate becomes more familiar (around World War 2), the book picks up and gets a lot more readable, so if you're reading it, stick with it. But if you're looking for a general book about probability, read Mlodinow's Drunkard's Walk (http://www.nytimes.com/2008/06/08/books/review/Johnson-G-t.html) first.