The Theory That Would Not Die
How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of C
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Average rating3.3
Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok. In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years -- at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information, even breaking Germany's Enigma code during World War II, and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security. Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time. - Publisher.
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Bayes theory is cute. Pop nonfiction math books seem incapable of being patronizing on one extreme or invoking their math theorem as an abstract magical spell on the other. I prefer the later, which is what this is. How did we find Russian submarines? We cast Bayes at them. Sometimes, even as someone very familiar with Bayes theorem I found these invocations impossible to understand what was literally happening, but overall, this is an easy and mathy read. 3.5 stars.
My reading speed asymptotically approached zero as this book progressed. Alas!
A history of science book, this covers the history of Bayesian statistics - from its origin in 18th century England via Thomas Bayes, to its second origin in 18th century France with Laplace, to its waxing and waning in popularity throughout the 20th century, and eventual redemption thanks to increased computational speed/processing power. A lot of the 20th century stuff seemed to be directly taken from interviews, so the book has an oral history quality to it. And it's certainly written in a lively, impassioned way.
But! It's also frustratingly vague of what “Bayesianism” actually IS (we do see Bayes' Rule once, early on in the Laplace chapter, and we have a lot of discussions of how people get upset about priors). But for a science book, I feel like it fails to convey the core insight of what makes Bayesian stats interesting. We talk a lot ABOUT Bayesian stats, but unlike e.g. Michio Kaku re: fancy physics (and maybe physicists will balk), or Emily Oster (or Esther Duflo and Abhijit Banerjee) and economics, or Michael Lewis and economics/anything, this book doesn't really inform the reader on the core idea. And I get it! It's hard. And I can't even pinpoint specifically HOW McGrayne doesn't get it right... Like, she definitely does spend a lot of time discussing priors, uncertainty, beliefs/probabilities, and frequentist's contrasting ideas of multiple samples (and how who has time/money to do that?!). But it almost feels like she spends a lot of time discussing these topics without ever first defining them in great depth.
Similarly, another thing which started to really grate on me was the long, comma-separated lists of applications of Bayesian stats (lists and lists and lists), as well as McGrayne's tendency to introduce numerous people in the same way: Robert (“Bob”) Smith, who received a PhD from Ivy League University, blah blah. Do I need to know he went by Bob? Also, these appeals to authority (PhD from Ivy League!) definitely worked on me in the beginning (“wow, Bayes is so mathy, these people are so well-qualified”), but then I started to be like, “wait, why do I need to know Bob goes by Bob and went to Yale if I'm never going to hear from him again?”
I was also was a little miffed by the relatively cursory mention of Bayes's history in economics (“oh yeah, Tversky and Kahneman won the Nobel for showing people aren't super rational”), while we spent - relatively - soooooo much time with military applications. And I mean, I like the Navy! I like to read about naval things! But I also became enamored with Bayes, like a lot of people in my classes, when going through the econ journey of game theory, risk and uncertainty, decision making under risk and uncertainty, and finally behavioral economics. This is a wonderful journey! A very interesting realm of study! So yeah, I was miffed that in a big book about Bayes, we spent so much time at Harvard Business School and the Navy, and so little time with, indeed, Kahneman and Tversky.
