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

"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.

This book moves through the history (so far) of the development and application of Bayes rule. It is a good story, and the book is well written. Unfortunately, it is somewhat mixed in the manner material is presented. For example, the author provides significant detail on the application of the rule to activities such as code cracking and finding submarines but she then goes on to list a large number of more recent applications with very little historical background. Maintaining consistency of depth for each application discussed would have significantly improved the "story". I would recommend this book to anyone who is interested in the history of science, statistics and mathematics, but be prepared for a "patchy" read.

Sharon Bertsch Mcgrayne is a talented science writer whose portraits of great scientists of the past are incisive and entertaining. However, she evidently believes that one must studiously avoid dealing with any serious scientific issues in entertaining a popular audience. For this reason, this book is a total failure. Why should a reader care about the history of an idea of which he or she has zero understanding? Mcgrayne turns the history of Bayes rule into a pitched battle between intransigent opponents, but we never find out what the real issue are.

In fact, Bayes rule is a mathematical tautology, being the definition of conditional probability. Suppose A is an event with probability P(A) and B is an event with probability P(B). Let C be the event "both A and B occur." Then the conditional probability P(A|B) of event A, given that we know that B has occurred, just P(C)/P(B). Moreover, if a decision-maker knows P(A), P(B), and P(C), and discovers that B occurred, then he should revise the probability that A occurred to P(A|B) = P(C)/P(B). Why? Well, suppose we have a population of 1000 individuals, where the probability that an event E is true of an individual is P(E), where E is any one of A, B, and C. Then the expected number of individuals for which B is true is 1000*P(B). Of these, the number for which A is also true is 1000*P(A). Therefore, the probability that an individual satisfies A, given that he satisfies B, is 1000*P(A)/1000*P(B) = P(A|B).

For instance, suppose 5% of the population uses drugs, and there is a drug test that is correct 95% of the time: it tests positive on a drug user 95% of the time, and it tests negative on a drug nonuser 95% of the time. If an individual tests positive, we can show using Bayes rule that the probability of his being a drug user is 50%. To see this, let A be the event "subject uses drugs," and let B be the event "subject test positive for using drugs." First, what is the probability P(B) of event B? Well, take a random subject. With probability 1/20 he is a drug user, so with probability (19/20)(1/20)=19/400 he is a drug user testing positive. With probability 19/20 he not a drug user, so he is a non-user testing positive with probability (1/20)(19/20)=19/400. Thus P(B) = 19/400+19/400=38/400. Let event C be "subject uses drugs and tests positive for using drugs." This probability is (1/20) times (19/20) = 19/400. Thus P(A|B) = P(C)/P(B) = 1/2.

If this seems mystifying, consider the following interpretation. Suppose we test 10000 people. The expected number of drug users will be 500, and 95% of them, or 475, will test positive for drug use. But 9500 people will be non-drug users, and 5% of them will erroneously test positive for drug use, which is 475 people. Thus, 50% of those who test positive for drugs are actually drug users.

The real brilliance of Bayes Rule lies in the fact that sometimes we want to find P(A|B) when we don't know either P(C) or P(B), but we do know P(B|A) and P(A). For instance, want to know P(A|B), meaning the probability that an individual who test positive is actually a drug user, but we only know the frequency P(A) of drug use in the population (5%) and the accuracy of the test, which is P(B|A) = 95% (a drug user tests positive with probability 0.95). Then we can write P(A|B)P(B) = P(C)=P(B|A)P(A). From the first and third terms we get P(A|B) = P(B|A)P(A)/P(B). In our case, this gives P(A|B) = 0.95(0.05)/P(B)=0.0475/P(B). But we can also calculate P(B) as follows.

Let N mean "A is false for the subject." Thus P(N) = 1-P(A) = 0.95. Then we have
P(B) = P(B|A)P(A) + P(B|N)P(N), as can be verified by simply multiplying out the right hand side of the equation. Thus in our case we have, given that we know that P(B|N) = 0.95 (the test accurately predicts that a non-user is a non-user with probability 0.95), so we have P(B) = 0.95(0.05) + 0.05(0.95) = 0.095. Thus P(A|B) = 0.0475/0.095 = 1/2.

Isn't this a simple and beautiful result? Only arithmetic and grade school algebra are used to arrive at this stunning result. By the way, for more on Bayes Rule, see my textbook Game Theory Evolving (Princeton 2009).

Now, who could dispute this analysis? It is clearly correct. So where does all of the vehement opposition to Bayes rule come from? The answer is that when a group of individuals (e.g., professional scientists) do not agree on P(A) then you cannot apply Bayes rule. You can however show that under many conditions, repeated observations of events A can lead to mutually acceptable values for P(A). For instance, suppose you know that the weight of a substance per ounce is variable and unknown, and each scientist i has his personal prior probability Pi that the weight is less than one gram per ounce. Suppose we take unbiased samples that are each about one ounce, and we take unbiased measurements of the weight. Then the long-run average of the sample weights will be accepted by all scientists as the updated probability. This is Bayesian updating.

However, it is not true that Bayesian updating always lead to convergence to a common probability distribution. See, for instance, papers by Mordecai Kurz, of Stanford University. Moreover, when observations are limited, the range of assessments of probabilities can be quite wide. This is why Bayes rule is considered "subjective." However, when we really know the probabilities, as in the case of the drug testing example, there is no controversy about the value of Bayes rule. It is extremely valuable, indeed indispensable, in such cases.

This book manages to obfuscate a very simple issue, turning sciences into a vast morality play. Now of course there are deep issues in the philosophy of probability that implicate Bayes rule, but one does not learn what they are from this book.