[[Sharon Bertsch McGrayne]] [[lit/kindle/The Theory That Would Not Die|Highlights]] McGrayne covers the history of Bayes Theorem and the war between frequentist and Bayesian schools of statistical thought, from the discovery of Bayes rule by Thomas Bayes in the 17th century, its later popularization by Laplace, its use in code breaking during World War II, and its myriad applications in more modern science since then. Across time, Bayes Theorem has been nearly lost many times, often discarded by those who used it in favor of frequentist methods or buried in secrecy by wartime classification. However, McGrayne illustrates how its usefulness across disciplines in the natural and social sciences have kept it alive. With the advent of modern computing power, the computational intractability of Bayes theorem has been overcome and its usefulness (and theoretical robustness) have become evident. If you enjoy the historical development of modern statistics and machine learning, this book is a quick overview. McGrayne writes for a general audience, eschewing even the most basic forays into the mathematical underpinnings of Bayesian inference, which I thought might help understand the reasons why Bayesian methods were maligned and discarded was often left unexplained. If you want a more in-depth (and opinionated) take, check out [[Bernoulli's Fallacy]] instead.