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Statistics and Analysis of Scientific Data (Second Edition)

55 to mention the Pitman-Koopman(-Darmois) lemma and its consequences, namely that the existence of conjugacy remains an epiphenomenon, making the number of pages dedicated to Gamma, Dirichlet, and Wishart distributions somewhat excessive. In Chapter 9, I enjoyed seeing Jay use the same admissible (non-)estimator as my favorite, namely the constant value estimator with value 3 (rather than the predictable 42). I wonder at the relevance of having a 10-line section like Section 9.3, when there are delicate issues in handling meta-analysis, even in a Bayesian mood (or mode). In the model uncertainty section, Jay discusses the (im)pertinence of both selecting one of the models and setting independent priors on their respective parameters, with which I disagree on both levels, although this is followed by a more-reasonable perspective on utility. It is also nice to see a new section about causation, but I would have welcomed an insert on the recent and somewhat-outrageous stand of Pearl (and MacKenzie) on statisticians missing the point on causation and counterfactuals by miles. “Nonparametric Bayes” is a new section, explicitly inspired by Ghahramani (2005), but while it mentions Gaussian and Dirichlet processes, I fear it comes up short in enticing the reader to truly grasp the meaning of a prior on functions, besides mentioning it exists. I am thus unsure of the utility of this section. This is one of the rare instances where measure theory is discussed, only to state it is beyond the scope of the book (p. 355). Chapter 10 is mostly dedicated to MCMC and contains a short section (10.7) about Hamiltonian Monte Carlo, with a mention of Stan (typed STAN as an acronym rather than Ulam’s first name, which will almost certainly make Andrew cringe, just as Betancourt spelled as Bentencourt will make Michael cringe). Partial derivatives are written  rather than ∂, a point I missed in my earlier review. The section about and against “objective” Bayesian methods (found in “Old ideas,” Chapter 12) has been reduced quite a lot from the earlier version, which does not displease me in the least. The book concludes with the nice aphorism that “Statistics is never having to say you are certain” (p. 462). Statistics and Analysis of Scientific Data (Second Edition)