Review: 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
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 by Sharon Bertsch McGrayne
My rating: 4 of 5 stars
It probably takes a special sort of person to dive into an entire book about one statistical theory, but for those so-motivated, this one pays off.
The pro’s: The author has done a phenomenal job at capturing and richly detailing the very “large” personalities that have championed (or condemned) the use of Bayes’ Rule through the centuries, amidst a little-known and long-simmering war that has persisted between statistical Bayesians and frequentists since the concept was first brought forward. This is even more impressive as she is a journalist, rather than a statistician. McGrayne immerses the reader in what can only be called “lush” detail of the history, from personalities to global events.
The con’s: This a very dense text. Not dry in an academic sense, but a lot of material to consume. At times I had to summon extra reserves of motivation to proceed to the next chapter. The topic is also a difficult one to communicate solely through narrative – more than once I found myself wishing for just a little bit of math-by-the-way-of-example to help grasp the concepts. (With such, this could actually serve well as an educational vehicle). While already familiar with Bayes, the application in some of the historical examples was, for me, elusive.
Computing power has today made the Bayesian/frequentist conflict somewhat moot, and I found myself wishing for a little more exposition of Bayesian applications in the modern era. (To me, this is where the real excitement lies, if “excitement” is the correct term!)
Overall – if statistics, scientific inference, decision theory or machine learning excite you, this is probably a book to have under your belt. Reading the history of Bayesian vs frequentist wars triggered some good musing and reflection on the critical question of “how to make inferences when too little, rather than too much, data are at hand”.