Archive for Chapman & Hall

Bayesian Workflow [book reviews]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on August 5, 2026 by xi'an

I thought I had posted a full-length review of the Bayesian Workflow, but the fact is I haven’t! Here is the blurb I wrote (upon request) for Chapman & Hall (and reproduced on Andrew’s blog):

“This original, thought-provoking, and transformative book is much much more than an implementation manual for Bayesian Data Analysis, even though it shares almost the same perspective. (The first sentence of the book states that the authors’ “conceptions of statistical practice, and of Bayesian statistics, have changed over the years”.) By providing a modus vivendi for undertaking Bayesian modelling from scratch in realistic settings where models are not magicked out of the blue, the authors explicit and rationalise the many steps required by such a bottom-up modelling protocol (“not a checklist, not a cookbook”, and not a flowchart!) in real situations. The contents read very well and very smoothly, with a seamless conjunction of intuition, modelling advices, computational details, and comparison tools. While unsurprisingly Bayesian, the perspective adopted therein remains both open and inclusive, with a welcome humility about the limitations and challenges of Bayesian workflows. This book should thus appeal to and profit a wide variety of readers, as providing guidance through an extensive collection of highly detailed examples, with shared code and exercises.”

Bayesian Workflow [cover]

Posted in Books, pictures, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , on April 25, 2026 by xi'an

Ah great, the new book on Bayesian workflow by Andrew Gelman, Aki Vehtari, Richard McElreath I knew they were working on is about to appear!  With entries from several coauthors and half of the chapters on case studies. I have not (yet) looked at its contents in detail…

the polls weren’t wrong [alt book review]

Posted in Statistics with tags , , , , , , , , , , , , , , , on April 1, 2025 by xi'an

handbook of sharing confidential data [book review]

Posted in Statistics with tags , , , , , , , , , , , , , on March 12, 2025 by xi'an

A new Chapman & Hall handbook appeared on the most current issue of confidentiality and privacy, which has been edited by Jörg Drechsler, Daniel Kifer, Jerome Reiter, and Aleksandra Slavković. The forty authors of the 18 chapters are mostly from the U.S., with a few outliers from Edinburgh (involved in two chapters on protecting the Scottish Longitudinal Study and the U.S. IRS tax data) and Tallinn (for a chapter on secure multi-party computation applications). This means a more U.S. centric focus for realistic implementations as, e.g., with the Census Bureau (which employs 25% of the authors), than those implied by EU regulations, for instance.

Overall, I enjoyed reading these chapters and would certainly use the book as a first entry to a graduate course on privacy (as opposed to some books I recently reviewed). The first two chapters are 100% formula-free and thus more surveys than informative entries to the field, imho. The following Part II on formal privacy techniques covers the expected standards of differential privacy, local vs. global design, single vs. multiple queries, consequence on learning machines and statistical procedures. Concerning Bayesian aspects, Chapter 7 about private machine learning has two paragraphs on the privacy properties of MCMC algorithms albeit not exposing clearly enough that privacy vanishes as the number of iterations grows to infinity. Chapter 8 concentrates on statistical differential privacy, much along my own perception of the requirements for a genuine statistical approach, with Bayesian aspects not sidelined. If less critical of differential privacy than I. Chapter 9 focusses on system issues, investing a dozen pages into the specifics of pseudo-random generators. Part III is about synthetic data, with some overlap between the first two chapters. (I would deem DP need not be introduced by Chapter 12.) I find the section rather superficial, mostly formula free, and lacking in the statistical impact.

As an aside, I am disappointed at the poor rendering of (mathematical) equations making me wonder which type of LaTeX, if any, was used. There are even genuine typos  that seem to result from cut and past encoding errors (see, e.g., the final accentuated c of Sklavković). The reference lists are plentiful, see e.g. the 164 entries for Chapter 7, to the point it would have made more sense to regroup them into a single bibliography. (The predictable reply being that chapters are sold separately and need their respective reference lists.)

[Disclaimer about potential self-plagiarism: this post or an edited version of it could possibly appear in my Books Review section in CHANCE.]

Bayesian Inference: Theory, Methods, Computations [book review]

Posted in Statistics with tags , , , , , , , , , , , , , , , , , , , , , on November 12, 2024 by xi'an

Bayesian Inference: Theory, Methods, Computations by Silvelyn Zwanzig and Rauf Ahmad, both from Uppsala University, is a recent book published by Chapman & Hall / CRC Press. About 300p long (plus appendices), it covers the core aspects of Bayesian inference, namely the decision theoretic motivations, its asymptotic validation, the specifics of estimation and testing, and the computational approximations (MC, MCMC, ABC, VB), with entries on prior specification and Normal linear models. And some R codes. It is (and feels like) constructed from Master and PhD courses (at Uppsala University), with a rigorous mathematical presentation and many examples, some related to biostatistics. Drawings from the first author’s daughter are included in most chapters, to this reviewer’s bemusement. From a further personal viewpoint, the book also reads rather close to my (Bayesian) choice of a Bayesian textbook, which proves rather accurate since several chapters are inspired by my own Bayesian Choice. as acknowledged therein. As well as by the more recent Statistical Decision Theory: Estimation, Testing, and Selection by Liese & Miescke (2008) and Introduction to the Theory of Statistical Inference by Liero & Zwanzig (2011). Witness, for instance, an example of prior construction for capture-recapture experiments on lizards as analysed by my PhD student Dupuis (1995) [with a curious switch to the authors on p.263] and  also included in The Bayesian Choice (with drawing 2.9 incorrect in that the lizards there have marks on their backs, instead of the code adopted by the ecologists, namely cutting one specific phalange for each capture).

Other minor quandaries: The usual issue of quoting the wrong edition for creating a method, as when citing Jeffreys (1946) for inventing non-informative priors [p.53], failing to point out the parameterisation invariance of intrinsic losses [p.95]considering that Bayes factors are only relevant for obtaining evidence against the null hypothesis [p.216], recommending BIC and DIC (!) [pp.232-6], advocating sampling importance resampling (SIR) for approximate sampling from the target (omitting infinite variance issues) [p.253], defining annealing as using “several trial distributions” [p.261], a mistake in ABC-MCMC [p.274] since the case when the simulated data is too far from the actual data should lead to a repetition rather than a pure rejection.

All in all, a reasonable textbook with some recent input, but still lacking in originality, if I may subjectively say so.

[Disclaimer about potential self-plagiarism: this post or an edited version of it could possibly appear in my Books Review section in CHANCE.]