**A**s indicated in an earlier MCMskv news, the scientific committee kept a session open for Breaking news! proposals, in conjunction with poster submissions. We received 21 proposals and managed to squeeze 12 fifteen minute presentations in an already tight program. (I advise all participants to take a relaxing New Year break and to load in vitamins and such in preparation for a 24/7 or rather 24/3 relentless and X’citing conference!) Here are the selected presentations, with (some links to my posts on the related papers and) abstracts available on the conference website. Note to all participants that there are still a few days left for submitting posters!

## MCMskv, Lenzerheide, 4-7 Jan., 2016 [breaking news #6]

Posted in Kids, Mountains, pictures, Travel, University life with tags Bayesian computation, ISBA, Lenzerheide, MCMSki, MCMskv, Monte Carlo Statistical Methods, poster, Switzerland on December 2, 2015 by xi'an## reproducibility

Posted in Books, Statistics with tags blinding, data privacy, maths house, Nature, Red State Blue State, reproducible research, Royal Statistical Society, Sally Clark, University of Warwick on December 1, 2015 by xi'an**W**hile in Warwick this week, I borrowed a recent issue (Oct. 08, 2015) of Nature from Tom Nichols and read it over diners in a maths house. Its featured topic was *reproducibility*, with a long initial (or introductory) article about “Fooling ourselves”, starting with an illustration from Andrew himself who had gotten a sign wrong in one of those election studies that are the basis of Red State, Blue State. While this article is not bringing radically new perspectives on the topic, there is nothing shocking about it and it even goes on mentioning Peter Green and his Royal Statistical Society President’s tribune about the Sally Clark case and Eric-Jan Wagenmakers with a collaboration with competing teams that sounded like “putting one’s head on a guillotine”. Which relates to a following “comment” on crowdsourcing research or data analysis.

I however got most interested by another comment by MacCoun and Perlmutter, where they advocate a systematic blinding of data to avoid conscious or unconscious biases. While I deem the idea quite interesting and connected with anonymisation techniques in data privacy, I find the presentation rather naïve in its goals (from a statistical perspective). Indeed, if we consider data produced by a scientific experiment towards the validation or invalidation of a scientific hypothesis, it usually stands on its own, with no other experiment of a similar kind to refer to. Add too much noise and only noise remains. Add too little and the original data remains visible. This means it is quite difficult to calibrate the blinding mechanisms in order for the blinded data to remain realistic enough to be analysed. Or to be different enough from the original data for different conclusions to be drawn. The authors suggest blinding being done by a software, by adding noise, bias, label switching, &tc. But I do not think this blinding can be done blindly, i.e., without a clear idea of what the possible models are, so that the perturbed datasets created out of the original data favour more one of the models under comparison. And are realistic for at least one of those models. Thus, some preliminary analysis of the original or of some pseudo-data from each of the proposed models is somewhat unavoidable to calibrate the blinding machinery towards realistic values. If designing a new model is part of the inferential goals, this may prove impossible… Again, I think having several analyses run in parallel with several perturbed datasets quite a good idea to detect the impact of some prior assumptions. But this requires statistically savvy programmers. And possibly informative prior distributions.

## the philosophical importance of Stein’s paradox

Posted in Books, pictures, Statistics, University life with tags Bayesian Analysis, Bayesian Choice, Charles Stein, decision theory, frequentist inference, James-Stein estimator, loss functions, philosophy of sciences, Stein effect, Stein's phenomenon, Stephen Stigler on November 30, 2015 by xi'an**I** recently came across this paper written by three philosophers of Science, attempting to set the Stein paradox in a philosophical light. Given my past involvement, I was obviously interested about which new perspective could be proposed, close to sixty years after Stein (1956). Paper that we should actually celebrate next year! However, when reading the document, I did not find a significantly innovative approach to the phenomenon…

The paper does not start in the best possible light since it seems to justify the use of a sample mean through maximum likelihood estimation, which only is the case for a limited number of probability distributions (including the Normal distribution, which may be an implicit assumption). For instance, when the data is Student’s t, the MLE is not the sample mean, no matter how shocking that might sounds! (And while this is a minor issue, results about the Stein effect taking place in non-normal settings appear much earlier than 1998. And earlier than in my dissertation. See, e.g., Berger and Bock (1975). Or in Brandwein and Strawderman (1978).)

While the linear regression explanation for the Stein effect is already exposed in Steve Stigler’s Neyman Lecture, I still have difficulties with the argument in that for instance we do not know the value of the parameter, which makes the regression and the inverse regression of parameter means over Gaussian observations mere concepts and nothing practical. (Except for the interesting result that two observations make both regressions coincide.) And it does not seem at all intuitive (to me) that imposing a constraint should improve the efficiency of a maximisation program… Continue reading

## seveneves [book review]

Posted in Books, Kids with tags Anathem, echidna, Neal Stephenson, ROT13, Seveneves, Snow Crash, space opera on November 29, 2015 by xi'an**A**s the latest Neal Stephenson’s novel, I was waiting most eagerly to receive Seveneves (or SevenEves ). Now I have read it, I am a bit disappointed by the book. It is a terrific concept, full of ideas and concepts, linking our current society and its limitations with what a society exiled in space could become, and with a great style as well, but as far as the story itself goes I have trouble buying it! In short, there is too much technology and not enough psychology, too many details and not enough of a grand scheme… This certainly is far from being the best book of the author. When compared with Snow Crash, Cryptonomicon, Anathem, or Reamde for instance. Even the fairly long and meandering Baroque Cycle comes on top of this space opera à la Arthur Clarke (if only for the cables linking Earth and space stations at 36,000 kilometres…).

The basis of Seveneves is a catastrophic explosion of our Moon that leads to the obliteration of live on Earth within a range of two years. The only way out is to send a small number of people to a space station with enough genetic material to preserve the diversity of the Human species. Two-third of the book is about the frantic scramble to make this possible. Then Earth is bombarded by pieces of the Moon, while the inhabitants of the expanded space station try to get organised and to get more energy from iced asteroids to get out of the way, while badly fighting for power. This leads the crowd of survivors to eventually reduce to seven women, hence the seven Eves. Then, a five thousand year hiatus, and the last part of the book deals with the new Human society, hanging up in a gigantic sphere of space modules around the regenerated Earth, where we follow a team of seven (!) characters whose goal is not exactly crystal clear.

While most books by Stephenson manage to produce a good plot on top of fantastic ideas, with some characters developed with enough depth to be really compelling, this one is missing at the plot level and even more at the character level, maybe because we know most characters are supposed to die very early in the story. But they do look like caricatures, frankly! And behave like kids astray on a desert island. Unless I missed the deeper message… The construction of the spatial mega-station is detailed in such details that it hurts!, but some logistic details on how to produce food or energy are clearly missing. And missing is also the feat of reconstituting an entire Human species out of *seven* women, even with a huge bank of human DNAs. The description of the station five thousand years later is even more excruciatingly precise. At a stage where I have mostly lost interest in the story, especially to find very little differences in the way the new and the old societies operate. And to avoid spoilers, gur er-nccnevgvba bs gur gjb tebhcf bs crbcyr jub erznvarq ba Rnegu, rvgure uvqqra va n qrrc pnir be ng gur obggbz bs gur qrrcrfg gerapu, vf pbzcyrgryl vzcynhfvoyr, sbe ubj gurl pbhyq unir fheivirq bire gubhfnaqf bs lrnef jvgu ab npprff gb erfbheprf rkprcg jung gurl unq cnpxrq ng gur ortvaavat… It took me some effort and then some during several sleepless nights to get over this long book and I remain perplexed at the result, given the past masterpieces of the author.

## superintelligence [book review]

Posted in Books, Statistics, Travel, University life with tags 2001: A Space Odyssey, AIs, artificial intelligence, Bill Gates, Chicken Run, doomsday argument, ethics, HAL, intelligence, Isaac Asimov, JSM 2015, morality, Nick Bostrom, Philip K. DIck, Seattle on November 28, 2015 by xi'an

“The first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.”I.J. Good

**I** saw the nice cover of Superintelligence: paths, dangers, strategies by Nick Bostrom [owling at me!] at the OUP booth at JSM this summer—nice owl cover that comes will a little philosophical fable at the beginning about sparrows—and, after reading an in-depth review [in English] by Olle Häggström, on Häggström hävdar, asked OUP for a review copy. Which they sent immediately. The reason why I got (so) interested in the book is that I am quite surprised at the level of alertness about the dangers of artificial intelligence (or computer intelligence) taking over. As reported in an earlier blog, and with no expertise whatsoever in the field, I was not and am not convinced that the uncontrolled and exponential rise of non-human or non-completely human intelligences is the number One entry in Doom Day scenarios. (As made clear by Radford Neal and Corey Yanovsky in their comments, I know nothing worth reporting about those issues, but remain presumably irrationally more concerned about climate change and/or a return to barbarity than by the incoming reign of the machines.) Thus, having no competence in the least in either intelligence (!), artificial or human, or in philosophy and ethics, the following comments on the book only reflect my neophyte’s reactions. *Which means the following rant should be mostly ignored! Except maybe on a rainy day like today…*

“The ideal is that of the perfect Bayesian agent, one that makes probabilistically optimal use of available information. This idea is unattainable (…) Accordingly, one can view artificial intelligence as a quest to find shortcuts…” (p.9)

Overall, the book stands much more at a philosophical and exploratory level than at attempting any engineering or technical assessment. The graphs found within are sketches rather than outputs of carefully estimated physical processes. There is thus hardly any indication how those super AIs could be coded towards super abilities to produce paper clips (but why on Earth would we need paper clips in a world dominated by AIs?!) or to involve all resources from an entire galaxy to explore even farther. The author envisions (mostly catastrophic) scenarios that require some suspension of belief and after a while I decided to read the book mostly as a higher form of science fiction, from which a series of lower form science fiction books could easily be constructed! Some passages reminded me quite forcibly of Philip K. Dick, less of electric sheep &tc. than of Ubik, where a superpowerful AI(s) turn humans into jar brains satisfied (or ensnared) with simulated virtual realities. Much less of Asimov’s novels as robots are hardly mentioned. And the third laws of robotics dismissed as ridiculously too simplistic (and too human). Continue reading

## in memoriam

Posted in Kids, pictures, Travel, Wines with tags 13 Novembre 2015, France, Paris on November 27, 2015 by xi'an## on the origin of the Bayes factor

Posted in Books, Statistics with tags Bayes factors, full Bayesian significance test, Haldane's prior, Harold Jeffreys, Jack Haldane, Jeffreys priors, non-informative priors, scientific inference on November 27, 2015 by xi'an**A**lexander Etz and Eric-Jan Wagenmakers from the Department of Psychology of the University of Amsterdam just arXived a paper on the invention of the Bayes factor. In particular, they highlight the role of John Burdon Sanderson (J.B.S.) Haldane in the use of the central tool for Bayesian comparison of hypotheses. In short, Haldane used a Bayes factor before Jeffreys did!

“The idea of a significance test, I suppose, putting half the probability into a constant being 0, and distributing the other half over a range of possible values.”H. Jeffreys

The authors analyse Jeffreys’ 1935 paper on significance tests, which appears to be the very first occurrence of a Bayes factor in his bibliography, testing whether or not two probabilities are equal. They also show the roots of this derivation in earlier papers by Dorothy Wrinch and Harold Jeffreys. [As an “aside”, the early contributions of Dorothy Wrinch to the foundations of 20th Century Bayesian statistics are hardly acknowledged. A shame, when considering they constitute the basis and more of Jeffreys’ 1931 *Scientific Inference*, Jeffreys who wrote in her necrology “I should like to put on record my appreciation of the substantial contribution she made to [our joint] work, which is the basis of all my later work on scientific inference.” In retrospect, Dorothy Wrinch should have been co-author to this book…] As early as 1919. These early papers by Wrinch and Jeffreys are foundational in that they elaborate a construction of prior distributions that will eventually see the Jeffreys non-informative prior as its final solution [*Jeffreys priors* that should be called *Lhostes priors* according to Steve Fienberg, although I think Ernest Lhoste only considered a limited number of transformations in his invariance rule]. The 1921 paper contains *de facto* the Bayes factor but it does not appear to be advocated as a tool *per se* for conducting significance tests.

“The historical records suggest that Haldane calculated the first Bayes factor, perhaps almost by accident, before Jeffreys did.” A. Etz and E.J. Wagenmakers

As another interesting aside, the historical account points out that Jeffreys came out in 1931 with what is now called Haldane’s prior for a Binomial proportion, proposed in 1931 (when the paper was read) and in 1932 (when the paper was published in the *Mathematical Proceedings of the Cambridge Philosophical Society)* by Haldane. The problem tackled by Haldane is again a significance on a Binomial probability. Contrary to the authors, I find the original (quoted) text quite clear, with a prior split before a uniform on [0,½] and a point mass at ½. Haldane uses a posterior odd [of 34.7] to compare both hypotheses but… I see no trace in the quoted material that he ends up using the Bayes factor as such, that is as his decision rule. (I acknowledge *decision rule* is anachronistic in this setting.) On the side, Haldane also implements model averaging. Hence my reading of this reading of the 1930’s literature is that it remains unclear that Haldane perceived the Bayes factor as a Bayesian [another anachronism] inference tool, upon which [and only which] significance tests could be conducted. That Haldane had a remarkably modern view of splitting the prior according to two orthogonal measures and of correctly deriving the posterior odds is quite clear. With the very neat trick of removing the infinite integral at p=0, an issue that Jeffreys was fighting with at the same time. In conclusion, I would thus rephrase the major finding of this paper as Haldane should get the priority in deriving the Bayesian significance test for point null hypotheses, rather than in deriving the Bayes factor. But this may be my biased views of Bayes factors speaking there…

Another amazing fact I gathered from the historical work of Etz and Wagenmakers is that Haldane and Jeffreys were geographically very close while working on the same problem and hence should have known and referenced their respective works. Which did not happen.