I enjoyed the book a lot; McGrayne has a good eye for the amusing details, and she conveys at least some of the intuition (although some graphs or examples would have helped the reader—I liked the flipping coin illustrations in Dasivia 2006 Bayesian Data Analysis). It’s also remarkably synoptic: I was repeatedly surprised by names popping up in the chronology, like BUGS, Bretthorst, Fisher’s smoking papers, Diaconis, the actuarial use of Bayes etc, and I have a better impression of Laplace and Good’s many contributions. The math was very light, which undermines the value of much of it since unless one is already an expert one doesn’t know how much the author is falsifying (for the best reasons), and means that some connections are missed (like empirical Bayes being a forerunner of hierarchical modeling, which aren’t well-explained themselves).
I enjoyed the book a lot; McGrayne has a good eye for the amusing details, and she conveys at least some of the intuition (although some graphs or examples would have helped the reader—I liked the flipping coin illustrations in Dasivia 2006 Bayesian Data Analysis). It’s also remarkably synoptic: I was repeatedly surprised by names popping up in the chronology, like BUGS, Bretthorst, Fisher’s smoking papers, Diaconis, the actuarial use of Bayes etc, and I have a better impression of Laplace and Good’s many contributions. The math was very light, which undermines the value of much of it since unless one is already an expert one doesn’t know how much the author is falsifying (for the best reasons), and means that some connections are missed (like empirical Bayes being a forerunner of hierarchical modeling, which aren’t well-explained themselves).