Sometime during the 1740s, the Reverend Thomas Bayes made the ingenious discovery that bears his name but then mysteriously abandoned it. It was rediscovered independently by a different and far more renowned man, Pierre Simon Laplace, who gave it its modern mathematical form and scientific application — and then moved on to other methods. Although Bayes’ rule drew the attention of the greatest statisticians of the twentieth century, some of them vilified both the method and its adherents, crushed it, and declared it dead. Yet at the same time, it solved practical questions that were unanswerable by any other means: the defenders of Captain Dreyfus used it to demonstrate his innocence; insurance actuaries used it to set rates; Alan Turing used it to decode the German Enigma cipher and arguably save the Allies from losing the Second World War; the U.S. Navy used it to search for a missing H-bomb and to locate Soviet subs; RAND Corporation used it to assess the likelihood of a nuclear accident; and Harvard and Chicago researchers used it to verify the authorship of the Federalist Papers. In discovering its value for science, many supporters underwent a near-religious conversion yet had to conceal their use of Bayes’ rule and pretend they employed something else. It was not until the twenty-first century that the method lost its stigma and was widely and enthusiastically embraced.

So begins Sharon McGrayne's fun new book, The Theory That Would Not Die, a popular history of Bayes' Theorem. Instead of reviewing the book, I'll summarize some of its content below. I skip the details and many great stories from the book, for example the (Bayesian) search for a lost submarine that inspired Hunt for Red October. Also see McGrayne's Google Talk here. She will be speaking at the upcoming Singularity Summit, too, which you can register for here (price goes up after August 31st).

Origins

In the 1700s, when probability theory was just a whiff in the air, the English Reverend Thomas Bayes wanted to know how to infer causes from effects. He set up his working problem like this: How could he learn the probability of a future event occurring if he only knew how many times it had occurred or not occurred in the past?

He needed a number, and it was hard to decide which number to choose. In the end, his solution was to just guess and then improve his guess later as he ...

Fabrice Pautot

Fabrice Pautot