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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.
Each chapter reads roughly as follows:
Joe Character was a maverick. He didn't really know much statistics. Then, one day he guessed at a probability of something occurring! He had never heard of Bayes or Bayesian statistics. But the frequentist man wanted to keep Joe down. Joe was sort of kept down, but maybe because of things that don't have much to do with philosophical interpretations of statistics.
The author seems to use such a broad net to catch stories of Bayesians that the unifying theme has no real meaning: anyone who has done statistics with some shortage of objective quantitative data, anyone who thought that probability could have more than one interpretation (there are a LOT in reality), and anyone who ever used Bayes' rule (which includes everyone who has ever done statistics or probability) is part of the author's oppressed Bayesian hero-force.
A little explanation of frequentist/propensity/subjective/classical interpretations of probability would have gone a long way in this book, as would some more careful clarifications of the terms used and categories the people were placed into. I recognize that the balance between history and dry textbook language could be tricky in a pop-math book, but this one erred so far away from discussion of the purported theme that it was almost about nothing.
Also, as a quick groan-inducing writing sample from the first chapter: "In 1771 he wrote a pamphlet---a kind of blog---declaring that..." Crimminy.
Thomas Bayes (1701–1761) was a Scottish clergyman who developed the technique. Basically, Bayesian statistics is a set of mathematical formulas where “one's inferences about parameters or hypotheses are updated as evidence accumulates.” Simply put, Bayes allows for our subjective inferences as the starting point of inquiry. Then, with accumulated evidence through testing, those initial assumptions are refined.
This sounds a great deal like our common sense approach to life, and it is. We all make hunches about probable outcomes of future events based on incomplete current information, and then change and alter our assumptions based on the results.
This book walks a fine line between a technical exposition of Bayesian statistics and a popular one. It does this to the point where I think many readers will feel like they are missing something --- as if the surface is only being skimmed. But the author had no choice; otherwise, the book would have gotten bogged down in technical details most readers can’t understand. So, this book has a fair balance between the two… if not somewhat thin in math while being thick in history!