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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 Hardcover – May 17, 2011
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“The Theory That Would Not Die is an impressively researched, rollicking tale of the triumph of a powerful mathematical tool.”—Andrew Robinson, Nature Vol. 475 (Andrew Robinson Nature Vol. 475 2011-07-28)
About the Author
Sharon Bertsch McGrayne is the author of numerous books, including Nobel Prize Women in Science: Their Lives, Struggles, and Momentous Discoveries and Prometheans in the Lab: Chemistry and the Making of the Modern World. She lives in Seattle.
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Top customer reviews
If you are generally familiar with the concept of Bayes' rule and the fundamental technical debate with frequentist theory, then I can wholeheartedly recommend the book because it will deepen your understanding of the history. The main limitation occurs if you are *not* familiar with the statistical side of the debate but are a general popular science reader: the book refers obliquely to the fundamental problems but does not delve into enough technical depth to communicate the central elements of the debate.
I think McGrayne should have used a chapter very early in the book to illustrate the technical difference between the two theories -- not in terms of mathematics or detailed equations, but in terms of a practical question that would show how the Bayesian approach can answer questions that traditional statistics cannot. In many cases in McGrayne's book, we find assertions that the Bayesian methods yielded better answers in one situation or another, but the underlying intuition about *why* or *how* is missing. The Bayesian literature is full of such examples that could be easily explained.
A good example occurs on p. 1 of ET Jaynes's Probability Theory: I observe someone climbing out a window in the middle of the night carrying a bag over the shoulder and running away. Question: is it likely that this person is a burgler? A traditional statistical analysis can give no answer, because no hypothesis can be rejected with observation of only one case. A Bayesian analysis, however, can use prior information (e.g., the prior knowledge that people rarely climb out wndows in the middle of the night) to yield both a technically correct answer and one that obviously is in better, common-sense alignment with the kinds of judgments we all make.
If the present book included a bit more detail to show exactly how this occurs and why the difference arises, I think it would be substantially more powerful for a general audience.
In conclusion: a good and entertaining book, although if you know nothing about the underlying debate, it may leave you wishing for more detail and concrete examples. If you already understand the technical side in some depth and can fill in the missing detail, then it will be purely enjoyable and you will learn much about the back history of the competing approaches to statistics.
I started reading a book about the history of Statistical Science, but I ended up with an impression that I just read an epic war and its heroes. People that, against all odds, stood for what they believe, because they saw further than their peers, that were still groping in the darkness of uncertainty. People with human aspirations and dilemas, like Alan Turing, who was murdered by British government and only recently has been acknowledged by his paramount deeds. ([...])
The author lays out an interesting narrative around the development and acceptance of Bayes' theorem, since its conception by Bayes and the contributions brought by Price, and later by Laplace (who was really endowed with a Da Vinci intellect). The theory dies and then revives several times and then, suddenly, a war breaks out inside Statistical Science: Fischer against Pearson and Neyman and everybody against Bayes: a 3-party war that, to my knowledge, has not ended until today.
Against all odds, Bayes theorem emerges as valid tool for modern applications. The author mention many ground breaking innovations sponsored by Bayes, and cites many important current contributors to the widespread use of Bayes (Dennis Lindley, HERO!; Amos Tversky & Daniel Kanehman, Nobel in Economics in 2002; Judea Pearl, ACM Alan Turing Prize in 2012; and many others).
Perk: if you are willing to spend 30 minutes to see a very funny comparison between Bayesian vs Frequentist approach, check out this class in Coursera (created by Dr. Mine Çetinkaya-Rundel for Data Analysis and Statistical Inference open course). You might be required to create a login: [...].
You can read books that attempt to explain the Bayes theorem but I got a better understanding of it from reading its many practical applications described in this book - from aiming guns to testing ammunition and building insurance company actuarial tables to name only a few.
When I attended two statistics courses in the 1970s, each dedicated about a week to Bayes' rule. I assumed this was a well-established and accepted rule of statistics. And I was not troubled that subjective probabilities are necessary when sufficient data are unavailable. I had no idea that Bayesian analysis was controversial. Now make my living in risk and decision analysis, with Bayes' rule at the core of "value of information" analysis.
What a compelling revealing story McGrayne tells! If I had read this book 40 years ago, my career might well have taken a different path.
The book is short of application details, and my hope is that some statistician will soon write a companion book about the calculation methods that evolved.