## the theory that would not die…

**A** few days ago, I had lunch with Sharon McGrayne in a Parisian café and we had a wonderful chat about the people she had met during the preparation of her book, ** the theory that would not die**. Among others, she mentioned the considerable support provided by Dennis Lindley, Persi Diaconis, and Bernard Bru. She also told me about a few unsavoury characters who simply refused to talk to her about the struggles and rise of Bayesian statistics. Then, once I had biked home, her book had at last arrived in my mailbox! How timely! (Actually, getting the book before would have been better, as I would have been able to ask more specific questions. But it seems the publisher, Yale University Press, had not forecasted the phenomenal success of the book and thus failed to scale the reprints accordingly!)

**H**ere is thus my enthusiastic (and obviously biased) reaction to ** the theory that would not die**. It tells the story and the stories of Bayesian statistics and of Bayesians in a most genial and entertaining manner. There may be some who will object to such a personification of science, which should be (much) more than the sum of the characters who contributed to it. However, I will defend the perspective that (Bayesian) statistical science is as much philosophy as it is mathematics and computer-science, thus that the components that led to its current state were contributed by individuals, for whom the path to those components mattered. While the book inevitably starts with the (patchy) story of Thomas Bayes’s life, incl. his passage in Edinburgh, and a nice non-mathematical description of his ball experiment, the next chapter is about “the man who did everything”, …, yes indeed, Pierre-Simon (de) Laplace himself! (An additional nice touch is the use of lower case everywhere, instead of an inflation of upper case letters!) How Laplace attacked the issue of astronomical errors is brilliantly depicted, rooting the man within statistics and explaining why he would soon move to the “probability of causes”. And rediscover plus generalise Bayes’ theorem. That his (rather unpleasant!) thirst for honours and official positions would cause later disrepute on his scientific worth is difficult to fathom, esp. when coming from knowledgeable statisticians like Florence Nightingale David. The next chapter is about the dark ages of [not yet] Bayesian statistics and I particularly liked the links with the French army, discovering there that the great Henri Poincaré testified at Dreyfus’ trial using a Bayesian argument, that Bertillon had completely missed the probabilistic point, and that the military judges were then all aware of Bayes’ theorem, thanks to Bertrand’s probability book being used at École Polytechnique! (The last point actually was less of a surprise, given that I had collected some documents about the involvement of late 19th/early 20th century artillery officers in the development of Bayesian techniques, Edmond Lhostes and Maurice Dumas, in connection with Lyle Broemeling’s Biometrika study.) The description of the fights between Fisher and Bayesians and non-Bayesians alike is as always both entertaining and sad. Sad also is the fact that Jeffreys’ masterpiece got so little recognition at the time. (While I knew about Fisher’s unreasonable stand on smoking, going as far as defending the assumption that “lung cancer might cause smoking”(!), the Bayesian analysis of Jerome Cornfield was unknown to me. And quite fascinating.) The figure of Fisher actually permeates the whole book, as a negative bullying figure preventing further developments of early Bayesian statistics, but also as an ambivalent anti-Bayesian who eventually tried to create his own brand of Bayesian statistics in the format of fiducial statistics…

“

…and then there was the ghastly de Gaulle.” D. Lindley

**T**he following part of ** the theory that would not die** is about Bayes’ contributions to the war (WWII), at least from the Allied side. Again, I knew most of the facts about Alan Turing and Bletchley Park’s Enigma, however the story is well-told and, as in previous occasions, I cannot but be moved by the waste of such a superb intellect, thanks to the stupidity of governments. The role of Albert Madansky in the assessment of the [lack of] safety of nuclear weapons is also well-described, stressing the inevitability of a Bayesian assessment of a one-time event that had [thankfully] not yet happened. The above quote from Dennis Lindley is the conclusion of his argument on why Bayesian statistics were not called Laplacean; I would think instead that the French post-war attraction for abstract statistics in the wake of Bourbaki did more against this recognition than de Gaulle’s isolationism and ghastliness. The involvement of John Tukey into military research was also a novelty for me, but not so much as his use of Bayesian [small area] methods for NBC election night previsions. (They could not hire José nor Andrew at the time.) The conclusion of Chapter 14 on why Tukey felt the need to distance himself from Bayesianism is quite compelling. Maybe paradoxically, I ended up appreciating Chapter 15 even more for the part about the search for a missing H-bomb near Palomares, Spain, as it exposes the plusses a Bayesian analysis would have brought.

“

There are many classes of problems where Bayesian analyses are reasonable, mainly classes with which I have little acquaintance.” J. Tukey

**W**hen approaching near recent times and to contemporaries, Sharon McGrayne gives a very detailed coverage of the coming-of-age of Bayesians like Jimmy Savage and Dennis Lindley, as well as the impact of Stein’s paradox (a personal epiphany!), along with the important impact of Howard Raiffa and Robert Schlaifer, both on business schools and on modelling prior beliefs [via conjugate priors]. I did not know anything about their scientific careers, but *Applied Statistical Decision Theory* is a beautiful book that prefigured both DeGroot‘s and Berger‘s. (As an aside, I was amused by Raiffa using Bayesian techniques for horse betting based on race bettors, as I had vaguely played with the idea during my spare if compulsory time in the French Navy!) Similarly, while I’d read detailed scientific accounts of Frederick Mosteller’s and David Wallace’s superb Federalist Papers study, they were only names to me. Chapter 12 mostly remedied this lack of mine’s.

“

We are just starting” P. Diaconis

**T**he final part, entitled Eureka!, is about the computer revolution we witnessed in the 1980’s, culminating with the (re)discovery of MCMC methods we covered in our own “history”. Because it contains stories that are closer and closer to today’s time, it inevitably crumbles into shorter and shorter accounts. However, ** the theory that would not die** conveys the essential message that Bayes’ rule had become operational, with its own computer language and objects like graphical models and Bayesian networks that could tackle huge amounts of data and real-time constraints. And used by companies like Microsoft and Google. The final pages mention neurological experiments on how the brain operates in a Bayesian-like way (a direction much followed by neurosciences, as illustrated by Peggy Series’ talk at Bayes-250).

**I**n conclusion, I highly enjoyed reading through ** the theory that would not die**. And I am sure most of my Bayesian colleagues will as well. Being Bayesians, they will compare the contents with their subjective priors about Bayesian history, but will in the end update those profitably. (The most obvious missing part is in my opinion the absence of E.T Jaynes and the MaxEnt community, which would deserve a chapter on its own.) Maybe ISBA could consider supporting a paperback or electronic copy to distribute to all its members! As an insider, I have little idea on how the book would be perceived by the layman: it does not contain any formula apart from [the discrete] Bayes’ rule at some point, so everyone can read through. The current success of

**shows that it reaches much further than academic circles. It may be that the general public does not necessarily grasp the ultimate difference between frequentist and Bayesians, or between Fisherians and Neyman-Pearsonians. However**

*the theory that would not die***goes over all the elements that explain these differences. In particular, the parts about single events are quite illuminating on the specificities of the Bayesian approach. I will certainly [more than] recommend it to all of my graduate students (and buy the French version for my mother once it is translated, so that she finally understands why I once gave a talk “Don’t tell my mom I am Bayesian” at ENSAE…!) If there is any doubt from the above, I obviously recommend the book to all Og’s readers!**

*the theory that would not die*
March 1, 2012 at 11:12 am

[…] to point out that the interview we made of Sharon McGrayne last summer is now on line in the new issue of CHANCE. Along with my […]

February 19, 2012 at 12:13 pm

[…] illustrating the strengths and limitations are tobacco vs. lung cancer, a clear case except for R.A. Fisher!, mobile phones vs. brain tumors, a not yet conclusive setting, hepatitis B vaccine vs. […]

February 16, 2012 at 12:13 am

[…] column in Le Monde (Sciences) had most unjustly escaped my attention: it mentioned Thomas Bayes on the very front page and I missed it till my latest breakfast! This article was written by a […]

January 4, 2012 at 4:49 pm

Significance posted a highly positive review by Michele Bottone.

December 26, 2011 at 6:53 pm

[…] odds (prior beliefs?) to win the day. I’m not sure I can give it as enthusiastic a review as Christian Robert, but I do recommend it as an engaging popular nonfiction read on this slice in the history of […]

October 25, 2011 at 3:31 pm

[…] nenhuma e, portanto, mais fácil do leitor com menos background matemático de acompanhar. O Christian Robert fez uma resenha do livro aqui. Li o livro interio e gostei bastante, mas eu sou obviamente viesado, […]

September 20, 2011 at 5:56 am

You mentioned in your review about brain experiments on Bayesian learning etc. It happens that Cosma Shalizi just wrote a piece in his blog in which he criticise some experiments that appear to show that people learn in a Bayesian way. Is it related to your comment? Any chance that you would comment Cosma Shalizi’s post?

here’s the link: http://cscs.umich.edu/~crshalizi/weblog/796.html

Btw, I can’t avoid to be amused by the fact that Gelman and Shalizi wrote a paper together about the philosophy of Bayesianism, since Shalizi is very critical of Bayesian philosophy in general and even Bayesian statistics…

September 20, 2011 at 6:30 am

Thanks Manoel, nice suggestion, however this would bring far too far from my home base… And I am struggling already to finish reading Error and Inference!

September 19, 2011 at 7:37 pm

I just bought my e-book. Thanks for the review!

September 19, 2011 at 10:36 pm

Great (I think I saw the purchase on Amazon!!! Many thanks!)

September 19, 2011 at 10:39 pm

Great! (And I think I saw the purchase on my amazon associate account, thanks!!!)