Archive for Sharon McGrayne

RSS conference in Newcastle

Posted in Books, pictures, Running, Statistics, Travel, University life with tags , , , , , , , on September 5, 2013 by xi'an

IMG_1697Although I could not stay at the RSS Annual Conference for the three days, I would have liked to do so, as there were several interesting sessions, from MCMC talks by Axel Finke, Din-Houn Lau, Anthony Lee and Michael Betancourt, to the session on Anti-fragility, the concept produced by Nassim Taleb in his latest book (reviewed before completion by Larry Wasserman). I find it rather surprising that the RSS is dedicating a whole session to this, but the usually anti-statistic stance of Taleb (esp. in The Black Swan) may explain for it (and the equally surprising debate between a “pro-Taleb” and a “pro-Silver”. I will also miss Sharon McGrayne‘s talk on the Bayesian revolution, but look forward to hear it at the Bayes-250 day in Duke next December. And I could have certainly benefited from the training session about building a package in R. It seemed, however, that one-day attendance was a choice made by many participants to the conference, judging from the ability to register for one or two days and from the (biased) sample of my friends.

Incidentally, the conference gave me the opportunity to discover Newcastle and Tynemouth, enjoying the architecture of Grey Street and running on the huge meadows almost at the city centre, among herds of cows in the morning fog. (I wish I had had more time to reach the neighbourly Hadrian wall and Durham, that I only spotted from the train to B’ham!)

big Bayes stories

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on July 29, 2013 by xi'an

(The following is our preface to the incoming “Big Bayes stories” special issue of Statistical Science, edited by Sharon McGrayne, Kerrie Mengersen and myself.)

Bayesian statistics is now endemic in many areas of scienti c, business and social research. Founded a quarter of a millenium ago, the enabling theory, models and computational tools have expanded exponentially in the past thirty years. So what is it that makes this approach so popular in practice? Now that Bayesian statistics has “grown up”, what has it got to show for it- self? In particular, what real-life problems has it really solved? A number of events motivated us to ask these questions: a conference in honour of Adrian Smith, one of the founders of modern Bayesian Statistics, which showcased a range of research emanating from his seminal work in the field, and the impressive book by Sharon McGrayne, the theory that would not die. At a café in Paris in 2011, we conceived the idea of gathering a similar collection of “Big Bayes stories”, that would demonstrate the appeal of adopting a Bayesian modelling approach in practice. That is, we wanted to collect real cases in which a Bayesian approach had made a significant di fference, either in addressing problems that could not be analysed otherwise, or in generating a new or deeper understanding of the data and the associated real-life problem.

After submitting this proposal to Jon Wellner, editor of Statistical Science, and obtaining his encouragement and support, we made a call for proposals. We received around 30 submissions (for which authors are to be warmly thanked!) and after a regular review process by both Bayesian and non-Bayesian referees (who are also deeply thanked), we ended up with 17 papers that reflected the type of stories we had hoped to hear. Sharon McGrayne, then read each paper with the utmost attention and provided helpful and encouraging comments on all. Sharon became part the editorial team in acknowledgement of this substantial editing contribution, which has made the stories much more enjoyable. In addition, referees who handled several submissions were asked to contribute discussions about the stories and some of them managed to fi nd additional time for this task, providing yet another perspective on the stories..

Bayesian Estimation of Population – Level Trends in Measures of Health Status Mariel M. Finucane, Christopher J. Paciorek, Goodarz Danaei, and Majid Ezzati
Galaxy Formation: Bayesian History Matching for the Observable Universe Ian Vernon, Michael Goldstein, and Richard G Bower
Estimating the Distribution of Dietary Consumption Patterns Raymond James Carroll
Bayesian Population Projections for the United Nations Adrian E. Raftery, Leontine Alkema, and Patrick Gerland
From Science to Management: Using Bayesian Networks to Learn about Lyngbya Sandra Johnson, Eva Abal, Kathleen Ahern, and Grant Hamilton
Search for the Wreckage of Air France Flight AF 447 Lawrence D Stone, Colleen M. Keller, Thomas M Kratzke, and Johan P Strumpfer
Finding the most distant quasars using Bayesian selection methods Daniel Mortlock
Estimation of HIV burden through Bayesian evidence synthesis Daniela De Angelis, Anne M Presanis, Stefano Conti, and A E Ades
Experiences in Bayesian Inference in Baltic Salmon Management Sakari Kuikka, Jarno Vanhatalo, Henni Pulkkinen, Samu Mäntyniemi, and Jukka Corander

As can be gathered from the table of contents, the spectrum of applications ranges across astronomy, epidemiology, ecology and demography, with the special case of the Air France wreckage story also reported in the paper- back edition of the theory that would not die. What made those cases so well suited for a Bayesian solution? In some situations, the prior or the expert opinion was crucial; in others, the complexity of the data model called for a hierarchical decomposition naturally provided in a Bayesian framework; and others involved many actors, perspectives and data sources that only Bayesian networks could aggregate. Now, before or (better) after reading those stories, one may wonder whether or not the “plus” brought by the Bayesian paradigm was truly significant. We think they did, at one level or another of the statistical analysis, while we acknowledge that in several cases other statistical perspectives or even other disciplines could have brought another solution, but presumably at a higher cost.

Now, before or (better) after reading those stories, one may wonder whether or not the \plus” brought by the Bayesian paradigm was truly signifi cant. We think it did, at one level or another of the statistical analysis, while we acknowledge that in several cases other statistical perspectives or even other disciplines could have provided another solution, but presumably at a higher cost. We think this collection of papers constitutes a worthy tribute to the maturity of the Bayesian paradigm, appropriate for commemorating the 250th anniversary of the publication of Bayes’ Essay towards solving a Problem in the Doctrine of Chances. We thus hope you will enjoy those stories, whether or not Bayesiana is your statistical republic.

Bayes 250th versus Bayes 2.5.0.

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , on July 20, 2013 by xi'an

More than a year ago Michael Sørensen (2013 EMS Chair) and Fabrizzio Ruggeri (then ISBA President) kindly offered me to deliver the memorial lecture on Thomas Bayes at the 2013 European Meeting of Statisticians, which takes place in Budapest today and the following week. I gladly accepted, although with some worries at having to cover a much wider range of the field rather than my own research topic. And then set to work on the slides in the past week, borrowing from my most “historical” lectures on Jeffreys and Keynes, my reply to Spanos, as well as getting a little help from my nonparametric friends (yes, I do have nonparametric friends!). Here is the result, providing a partial (meaning both incomplete and biased) vision of the field.

Since my talk is on Thursday, and because the talk is sponsored by ISBA, hence representing its members, please feel free to comment and suggest changes or additions as I can still incorporate them into the slides… (Warning, I purposefully kept some slides out to preserve the most surprising entry for the talk on Thursday!)

Bayes 250 in Durham

Posted in Books, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , on March 27, 2013 by xi'an

Reproducing an email from ISBA (sorry about the confusion purposely created by the title, this is Durham, North Carolina, not Durham, England, just as the London in Bayes 250 in London was London, England, not London, Ontario!):

ISBA announces a special celebration of the 250th anniversary of the presentation (December 23, 1763) of Thomas Bayes’ seminal paper “An Essay towards solving a Problem in the Doctrine of Chances” that will be held at Duke University in conjunction with the O-Bayes 13 Workshop (December 15-19) and EFab@ Bayes250 Workshop (December 15-17). (I am part of the scientific committee for O-Bayes 13!)

Speakers for the anniversary celebration are legendary contributors to the Bayesian literature, spanning a range of fields:

  • Stephen Fienberg, Carnegie-Mellon University
  • Michael Jordan, University of California, Berkeley
  • Christopher Sims, Princeton University
  • Adrian Smith, University of London
  • Stephen Stigler, University of Chicago

There will be a banquet in the evening, with a speech by Sharon Bertsch McGrayne, noted author of the popular book “The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines and Emerged Triumphant From Two Centuries of Controversy.”

Sharon McGrayne’s interview in CHANCE

Posted in Books, Statistics, University life with tags , , , , , on March 1, 2012 by xi'an

Just to point out that the interview we (editors of CHANCE) made of Sharon McGrayne last summer is now on line in the new issue of CHANCE. Along with my book review. Terrific! We are now preparing a similar interview with Persi Diaconis in connection with his book, Magical Mathematics, and my book review.

the theory that would not die…

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , on September 19, 2011 by xi'an

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!)

 Here 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

The 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

When 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

The 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).

In 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 the theory that would not die 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!

NYT obituary & reviews

Posted in Books, Statistics, University life with tags , , , , , on August 15, 2011 by xi'an

I was taking advantage of being in the US a Sunday to read the Sunday edition of the NYT yesterday and I came upon the obituary for Paul Meier, “the” Meier in the Kaplan-Meier estimator. (The NYT  actually notes that the corresponding JASA paper has been quoted more than 35,000 times!) I also learned from the NYT that Paul Meier was one of the first proponents of randomization in medical trials.

There was also an interesting review of George Martin’s  A Dance with Dragons that is waiting for me at home! Sailing the Bahamas last week means I missed the review of Sharon McGrayne’s the theory that would not die, written by John Paulos. Here is his concluding paragraph (I won’t comment since I have not read Sharon’s book yet!):

Statistics is an imperialist discipline that can be applied to almost any area of science or life, and this litany of applications is intended to be the unifying thread that sews the book into a coherent whole. It does so, but at the cost of giving it a list-like, formulaic feel. More successful are McGrayne’s vivifying sketches of the statisticians who devoted themselves to Bayesian polemics and counterpolemics. As McGrayne amply shows, orthodox Bayesians have long been opposed, sometimes vehemently, by so-called frequentists, who have objected to their tolerance for subjectivity. The nub of the differences between them is that for Bayesians the prior can be a subjective expression of the degree of belief in a hypothesis, even one about a unique event or one that has as yet never occurred. For frequentists the prior must have a more objective foundation; ideally that is the relative frequency of events in repeatable, well-defined experiments. McGrayne’s statisticians exhibit many differences, and she cites the quip that you can nevertheless always tell them apart by their posteriors, a good word on which to end.

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