Archive for Nobel Prize

the 101 favourite novels of Le Monde readers

Posted in Books, Kids, pictures with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on January 1, 2020 by xi'an

Le Monde called its readers to vote for their five favourite novels, with no major surprise in the results, except maybe Harry Potter coming up top. Before Voyage au bout de la nuit and (the predictable) A la recherche du temps perdu. And a complete unknown, Damasio’s La Horde du Contrevent, as 12th and first science fiction book. Above both the Foundation novels (16th). And Dune (32nd). And Hyperion Cantos (52). But no Jules Verne! In a sense, it reflects upon the French high school curriculum on literature that almost uniquely focus on French 19th and 20th books. (Missing also Abe, Conrad, Chandler, Dickens, Ishiguro, Joyce, Kawabata, Madame de Lafayette, Levi, Morante, Naipaul, Rabelais, Rushdie, Singer, and so many others…) Interestingly (or not), Sartre did not make it to the list, despite his literature 1953 Nobel Prize, maybe because so few read the (appalling) books of his chemins de la liberté trilogy.

I did send my vote in due time but cannot remember for certain all the five titles I chose except for Céline’s Voyage au bout de la nuit (2nd), Cormac McCarthy’s The Road (74th) and maybe Fedor Dostoievski’s Brothers Karamazov (24th). Maybe not as I may have included Barbey d’Aurevilly’s L’ensorcelée, Iain Pears’ An instance at the fingerpost, and Graham Greene’s The End of the affair, neither of which made it in the list. Here are some books from the list that would have made it to my own 101 list, although not necessarily as my first choice of titles for authors like Hugo (1793!) or Malraux (l’Espoir). (Warning: Amazon Associate links).

AIQ [book review]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , , , on January 11, 2019 by xi'an

AIQ was my Christmas day read, which I mostly read while the rest of the household was still sleeping. The book, written by two Bayesians, Nick Polson and James Scott, was published before the ISBA meeting last year, but I only bought it on my last trip to Warwick [as a Xmas present]. This is a pleasant book to read, especially while drinking tea by the fire!, well-written and full of facts and anecdotes I did not know or had forgotten (more below). Intended for a general audience, it is also quite light, from a technical side, rather obviously, but also from a philosophical side. While strongly positivist about the potential of AIs for the general good, it cannot be seen as an antidote to the doomlike Superintelligence by Nick Bostrom or the more factual Weapons of Maths Destruction by Cathy O’Neal. (Both commented on the ‘Og.)

Indeed, I find the book quite benevolent and maybe a wee bit too rosy in its assessment of AIs and the discussion on how Facebook and Russian intervention may have significantly to turn the White House Orange is missing [imho] the viral nature of the game, when endless loops of highly targeted posts can cut people from the most basic common sense. While the authors are “optimistic that, given the chance, people can be smart enough”, I do reflect on the sheer fact that the hoax that Hillary Clinton was involved in a child sex ring was ever considered seriously by people. To the point of someone shooting at the pizza restaurant. And I hence am much less optimistic at the ability for a large enough portion of the population, not even the majority, to keep a critical distance from the message carried by AI driven media. Similarly, while Nick and James point out (rather late in the book) that big data (meaning large data) is not necessarily good data for being unrepresentative at the population at large, they do not propose (in the book) highly convincing solutions to battle bias in existing and incoming AIs. Leading to a global worry that AIs may do well for a majority of the population and discriminate against a minority by the same reasoning. As described in Cathy O’Neal‘s book, and elsewhere, proprietary software does not even have to explain why it discriminates. More globally, the business school environment of the authors may have prevented them from stating a worry on the massive power grab by the AI-based companies, which genetically grow with little interest in democracy and states, as shown (again) by the recent election or their systematic fiscal optimisation. Or by the massive recourse to machine learning by Chinese authorities towards a social credit system grade for all citizens.

“La rage de vouloir conclure est une des manies les plus funestes et les plus stériles qui appartiennent à l’humanité. Chaque religion et chaque philosophie a prétendu avoir Dieu à elle, toiser l’infini et connaître la recette du bonheur.” Gustave Flaubert

I did not know about Henrietta Leavitt’s prediction rule for pulsating stars, behind Hubble’s discovery, which sounds like an astronomy dual to Rosalind Franklin’s DNA contribution. The use of Bayes’ rule for locating lost vessels is also found in The Theorem that would not die. Although I would have also mentioned its failure in locating Malaysia Airlines Flight 370. I had also never heard the great expression of “model rust. Nor the above quote from Flaubert. It seems I have recently spotted the story on how a 180⁰ switch in perspective on language understanding by machines brought the massive improvement that we witness today. But I cannot remember where. And I have also read about Newton missing the boat on the precision of the coinage accuracy (was it in Bryson’s book on the Royal Society?!), but with less neutral views on the role of Newton in the matter, as the Laplace of England would have benefited from keeping the lax measures of assessment.

Great to see friendly figures like Luke Bornn and Katherine Heller appearing in the pages. Luke for his work on the statistical analysis of basketball games, Katherine  for her work on predictive analytics in medicine. Reflecting on the missed opportunities represented by the accumulation of data on any patient throughout their life that is as grossly ignored nowadays as it was at Nightingale‘s time. The message of the chapter [on “The Lady with the Lamp”] may again be somewhat over-optimistic: while AI and health companies see clear incentives in developing more encompassing prediction and diagnostic techniques, this will only benefit patients who can afford the ensuing care. Which, given the state of health care systems in the most developed countries, is an decreasing proportion. Not to mention the less developed countries.

Overall, a nice read for the general public, de-dramatising the rise of the machines!, and mixing statistics and machine learning to explain the (human) intelligence behind the AIs. Nothing on the technical side, to be sure, but this was not the intention of the authors.

bootstrap in Nature

Posted in Statistics with tags , , , , , , , , , , on December 29, 2018 by xi'an

A news item in the latest issue of Nature I received about Brad Efron winning the “Nobel Prize of Statistics” this year. The bootstrap is certainly an invention worth the recognition, not to mention Efron’s contribution to empirical Bayes analysis,, even though I remain overall reserved about the very notion of a Nobel prize in any field… With an appropriate XXL quote, who called the bootstrap method the ‘best statistical pain reliever ever produced’!

Vidiadhar Surajprasad Naipaul (1932-2018)

Posted in Books, pictures, Travel with tags , , , , , , , , , , on August 13, 2018 by xi'an


I heard of the death of the writer V.S. Naipal late today, after arriving on the North coast of Vancouver Island. While not familiar with many of his books, I remember reading a House for Mr. Biswas and A Bend in the River in the mid 80’s, following a suggestion by my late friend José de Sam Lazaro, who was a professor in Rouen when I was doing my PhD there and with whom I would travel from Paris to Rouen by the first morning train… As most suggestions from José, it was an eye-opener on different views and different stories, as well as a pleasure to read the crisp style of Naipaul. Who thus remains inextricably linked with my memories of José. I also remember later discussing with, by postal letters, while in Purdue, on the strength of Huston’s The Dead, the last and possibly best novel of Joyce’s Dubliners, which stroke me as expressing so clearly and deeply the final feelings of utter failure of Conroy, Gretta’s husband. As well as his defense of Forman’s Amadeus!

look, look, confidence! [book review]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , on April 23, 2018 by xi'an

As it happens, I recently bought [with Amazon Associate earnings] a (used) copy of Confidence, Likelihood, Probability (Statistical Inference with Confidence Distributions), by Tore Schweder and Nils Hjort, to try to understand this confusing notion of confidence distributions. (And hence did not get the book from CUP or anyone else towards purposely writing a review. Or a ½-review like the one below.)

“Fisher squared the circle and obtained a posterior without a prior.” (p.419)

Now that I have gone through a few chapters, I am no less confused about the point of this notion. Which seems to rely on the availability of confidence intervals. Exact or asymptotic ones. The authors plainly recognise (p.61) that a confidence distribution is neither a posterior distribution nor a fiducial distribution, hence cutting off any possible Bayesian usage of the approach. Which seems right in that there is no coherence behind the construct, meaning for instance there is no joint distribution corresponding to the resulting marginals. Or even a specific dominating measure in the parameter space. (Always go looking for the dominating measure!) As usual with frequentist procedures, there is always a feeling of arbitrariness in the resolution, as for instance in the Neyman-Scott problem (p.112) where the profile likelihood and the deviance do not work, but considering directly the distribution of the (inconsistent) MLE of the variance “saves the day”, which sounds a bit like starting from the solution. Another statistical freak, the Fieller-Creasy problem (p.116) remains a freak in this context as it does not seem to allow for a confidence distribution. I also notice an ambivalence in the discourse of the authors of this book, namely that while they claim confidence distributions are both outside a probabilisation of the parameter and inside, “producing distributions for parameters of interest given the data (…) with fewer philosophical and interpretational obstacles” (p.428).

“Bias is particularly difficult to discuss for Bayesian methods, and seems not to be a worry for most Bayesian statisticians.” (p.10)

The discussions as to whether or not confidence distributions form a synthesis of Bayesianism and frequentism always fall short from being convincing, the choice of (or the dependence on) a prior distribution appearing to the authors as a failure of the former approach. Or unnecessarily complicated when there are nuisance parameters. Apparently missing on the (high) degree of subjectivity involved in creating the confidence procedures. Chapter 1 contains a section on “Why not go Bayesian?” that starts from Chris Sims‘ Nobel Lecture on the appeal of Bayesian methods and goes [softly] rampaging through each item. One point (3) is recurrent in many criticisms of B and I always wonder whether or not it is tongue-in-cheek-y… Namely the fact that parameters of a model are rarely if ever stochastic. This is a misrepresentation of the use of prior and posterior distributions [which are in fact] as summaries of information cum uncertainty. About a true fixed parameter. Refusing as does the book to endow posteriors with an epistemic meaning (except for “Bayesian of the Lindley breed” (p.419) is thus most curious. (The debate is repeating in the final(e) chapter as “why the world need not be Bayesian after all”.)

“To obtain frequentist unbiasedness, the Bayesian will have to choose her prior with unbiasedness in mind. Is she then a Bayesian?” (p.430)

A general puzzling feature of the book is that notions are not always immediately defined, but rather discussed and illustrated first. As for instance for the central notion of fiducial probability (Section 1.7, then Chapter 6), maybe because Fisher himself did not have a general principle to advance. The construction of a confidence distribution most often keeps a measure of mystery (and arbitrariness), outside the rather stylised setting of exponential families and sufficient (conditionally so) statistics. (Incidentally, our 2012 ABC survey is [kindly] quoted in relation with approximate sufficiency (p.180), while it does not sound particularly related to this part of the book. Now, is there an ABC version of confidence distributions? Or an ABC derivation?) This is not to imply that the book is uninteresting!, as I found reading it quite entertaining, with many humorous and tongue-in-cheek remarks, like “From Fraser (1961a) and until Fraser (2011), and hopefully even further” (p.92), and great datasets. (Including one entitled Pornoscope, which is about drosophilia mating.) And also datasets with lesser greatness, like the 3000 mink whales that were killed for Example 8.5, where the authors if not the whales “are saved by a large and informative dataset”… (Whaling is a recurrent [national?] theme throughout the book, along with sport statistics usually involving Norway!)

Miscellanea: The interest of the authors in the topic is credited to bowhead whales, more precisely to Adrian Raftery’s geometric merging (or melding) of two priors and to the resulting Borel paradox (xiii). Proposal that I remember Adrian presenting in Luminy, presumably in 1994. Or maybe in Aussois the year after. The book also repeats Don Fraser’s notion that the likelihood is a sufficient statistic, a point that still bothers me. (On the side, I realised while reading Confidence, &tc., that ABC cannot comply with the likelihood principle.) To end up on a French nitpicking note (!), Quenouille is typ(o)ed Quenoille in the main text, the references and the index. (Blame the .bib file!)