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

Sharon McGrayne writes popular non-fiction about science and the people who make it happen, and I believe this is her finest work. Well-written, deeply researched and at times, riveting, this book explores the discovery and early development of Bayes' Rule (Bayes' Theorem), a deceptively simple equation utilizing conditional probability. Though Bayes' Rule has been misunderstood, neglected,pooh-poohed and pilloried, it has stood the test of time and delivered many spectacular successes. Some of the most important uses of Bayes' Rule in history were cloaked in secrecy, and Ms. McGrayne has brought many of these to popular attention.On the research side, McGrayne lists literally hundreds of footnotes and references; these and the index comprise over 35% of the book. (As an aside, I was surprised when the book seemed to end early. Then I looked at the supporting material, and I understood.) She has also done many interviews, and correlated and connected lots of bits and pieces from thousands of resources. Although I was aware of several of the more spectacular examples, there were none that Ms. McGrayne didn't add substantially to my understanding. Some of the stories read like mysteries, and you're unable to put the book down until you read and appreciate the resolution.For statisticians, regardless of technical philosophy, this is a volume that you overlook at your peril. Though decidedly non-technical, it provides many useful cautions for both theoretical and practical statisticians; not the least of which involves hubris.I heartily endorse this book to all technical people, and I've also given personal recommendations to others, especially those interested in biography or history. Bayes' Rule has played a much larger role in making the world what it is today than most of us appreciate. This book goes a long way towards rectifying that unfamiliarity.

"The Theory That Would Not Die" is an enjoyable account of the history of Bayesian statistics from Thomas Bayes's first idea to the ultimate (near-)triumph of Bayesian methods in modern statistics. As a statistically-oriented researcher and avowed Bayesian myself, I found that the book fills in details about the personalities, battles, and tempestuous history of the concepts.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.

"The Theory That Would Not Die" about Bayes' Rule is one of the most interesting, non­technical, books about technology that I have ever read. If you want to use Bayes' Rule you should read the book to learn how others have used Bayes, but you won't learn the details. You will know where to look by the time you have finished and will also know where not to look for more information. Before going further I would like to state that I have no connection with the author, the publisher or an opponent of either. Sometimes I wonder about that when reading reviews. I am also not a mathematician. I bought the Kindle edition because of a layman's interest in voice-to-text programs. They all use a modern algorithm based on Bayes. The book is about one of the most useful general scientific tools of our time, but more than that it is about the hubris, arrogance and human frailties of mathematicians and scientists upon whom we rely for a better future. The book is like reading a mystery documentary of how eminent mathematicians tried to destroy those with whom they disagreed -- of childish behavior -- of lost opportunities for humanity and of how in the end humanity gained knowledge in many fields. There are many human lessons that can be learned from this book completely independent of Bayes and its utility. I give this book five stars without hesitation and feel a bit sorry for those people who have a serious interest in science who don't take the time to read it. I found it hard to put the book down and finished it in a few days. By the way: On my Kindle 38% of this book is devoted to several appendices, footnotes and a glossary.

Es muy interesante y la lectura es amena. Además el libro en cuestión es bastante bonito.

I bought this book because I was interested in knowing more about Bayes theorem/Rule, not about what it is but how it came to be, its applications, history etc. Loved the book. Don''t buy if you want to learn the math of Bayes theorem.

Computational 'grunt' turned an interesting, controversial approach to predictive statistical methods (labelled Bayes or LaPlace) into the backbone of today's forecasting.To be an analyst purporting to understand and apply data in today's world, and not understand the concepts in this book, is to be as ignorant as those who continued to believe in a flat earth when the mathematics clearly showed otherwise.The concepts are mind changing. The case examples are mind blowing. The history is fascinating and the combative personalties makes a lot of politics look tame.

Overview of the history up until Markov Chain Monte Carlo methods. The book even helped clarify a few points about the application of Bayes' rule.

I was a graduate student in statistics at Virginia Tech. about 1970. Then we knew that prof. I. J. Good would sometimes refer to conversations he'd once had with Alan Turing. Later it was revealed that these had taken place at Bletchley Park, the top secret WWII code breaking facility. Good was an avid Bayesian. Thanks to this book I now know that his enthusiasm was the result of his experience at Bletchley.The book gives a superb account of the history of Bayesian methods from their inception until their blossoming in the recent decades of the computer age.