Archive for earthquake

BIRS call for Oaxaca

Posted in Kids, Travel, University life with tags , , , , , , , on September 26, 2017 by xi'an

Here is a call for support from Nassim Goussoub, Scientific Director of BIRS:I would  like to call upon you to consider aiding the people of the State of Oaxaca. As you may know, through their support for BIRS-CMO, the people of Oaxaca have welcomed the World’s mathematical sciences community with open arms. With the plans to build a permanent facility under way, they are destined to be our hosts for years to come. I therefore ask you to contribute — if you can. Here are some of the foundations accepting donations.

  1. Francisco Toledo’s Foundation, IAGO (Instituto Artes Gráficas de Oaxaca)

  2. International Community Foundation(ICF)

  3. Global Giving

  4. Red Cross Mexico 6. Unicef Mexico

the fifth season [book review]

Posted in Books, Kids, Travel with tags , , , , , , , on May 14, 2017 by xi'an

When in Oxford two months ago, I dropped by the original Blackwell bookstore on my way to the station and rather hurriedly grabbed a few books from the science-fiction and fantasy section! One of them was The Fifth Season by N.K. Jemisin, which sounded exciting [enough] from the back cover and gave a sort of reassurance from the Hugo Award label on the front cover.

While I end up being rather disappointed with the whole book, there are redeeming features, from the universe conception, where massive earthquakes destroy civilisations now and then and where some races can locally control or unravel telluric forces, to the multifaceted conception of the story, with three women blessed or plagued with this ability, to the exposition of the exploitation of those women by the ruling class and the rejection by most of their society. This ends up however too much of a ping-pong game, when moving from one character to another character is more and more of a nuisance, with a predictable reunification of the three viewpoints at the end and just too many deus ex machina moments, even for people controlling earthquakes.

Coincidentally [not really!], the author, N.K. Jemisin, also happens to be the science-fiction and fantasy book editor for The New York Times, with a compilation of her favourite titles every trimester or so. And a tendency towards short stories, anthologies and graphic novels that makes the entries mildly appealing to me. But still managed to signal to me a recent publishing of some short stories by Ursula Le Guin.

unmistakable hints of being in the US

Posted in pictures, Travel with tags , , , , , , , , on August 27, 2016 by xi'an

the signal and the noise

Posted in Books, Statistics with tags , , , , , , , , , , , on February 27, 2013 by xi'an

It took me a while to get Nate Silver’s the signal and the noise: why so many predictions fall – but some don’t (hereafter s&n) and another while to read it (blame A Memory of Light!).

“Bayes and Price are telling Hume, don’t blame nature because you are too daft to understand it.” s&n, p.242

I find s&n highly interesting and it is rather refreshing to see the Bayesian approach so passionately promoted by a former poker player, as betting and Dutch book arguments have often been used as argument in favour of this approach. While it works well for some illustrations in the book, like poker and the stock market, as well as political polls and sports, I prefer more decision theoretic motivations for topics like weather prediction, sudden epidemics, global warming or terrorism. Of course, this passionate aspect makes s&n open to criticisms, like this one by Marcus and Davies in The New Yorker about seeing everything through the Bayesian lenses. The chapter on Bayes and Bayes’ theorem (Chapter 8) is a wee caricaturesque in this regard. Indeed, Silver sees too much in Bayes’ Essay, to the point of mistakenly attributing to Bayes a discussion of Hume’s sunrise problem. (The only remark is made in the Appendix, which was written by Price—like possibly the whole of the Essay!—, and  P.S. Laplace is the one who applied Bayesian reasoning to the problem, leading to Laplace’s succession rule.) The criticisms of frequentism are also slightly over-the-levee: they are mostly directed at inadequate models that a Bayesian analysis would similarly process in the wrong way. (Some critics argue on the opposite that Bayesian analysis is too much dependent on the model being “right”! Or on the availability of a fully-specified  model.) Seeing frequentism as restricted to “collecting data among just a sample of the population rather than the whole population” (p.252) is certainly not presenting a broad coverage of frequentism.

“Prediction serves a very central role in hypothesis testing, for instance, and therefore in all of science.” s&n, p.230

The book is written in a fairly enjoyable style, highly personal (no harm with that) and apart from superlativising (!) everyone making a relevant appearance—which seems the highest common denominator of all those pop’sci’ books I end up reviewing so very often!, maybe this is something like Rule #1 in Scientific Writing 101 courses: “makes the scientists sound real, turn’em into real people”—, I find it rather well-organised as it brings the reader from facts (prediction usually does poorly) to the possibility of higher quality prediction (by acknowledging prior information, accepting uncertainty, using all items of information available, further accepting uncertainty, &tc.). I am not sure the reader is the wiser by the end of the book on how one should improve one’s prediction tools, but there is a least a warning about the low quality of most predictions and predictive tools that should linger in the reader’s ears…. I enjoyed very much the chapter on chess, esp. the core about Kasparov’s misreading the computer reasons for a poor move (no further spoiler!), although I felt it was not much connected to the rest of the book.

In his review, Larry Wasserman argues that the defence Silver makes of his procedure is more frequentist than Bayesian. Because he uses calibration and long-term performances. Well… Having good calibration properties does not mean the procedure is not Bayesian or frequentist, simply that it is making efficient use of the available information. Anyway, I agree (!) with Larry on the point that Silver somehow “confuses “Bayesian inference” with “using Bayes’ theorem”. Or puts too much meaning in the use of Bayes’ theorem, not unlike the editors of Science & Vie a few months ago. To push Larry’s controversial statement a wee further, I would even wonder whether the book has anything to do about inference. Indeed, in the end, I find s&n rather uninformative about statistical modelling and even more (or less!) about model checking. The only “statistical” model that is truly discussed over the book is the power law distribution, applied to earthquakes and terrorist attack fatalities. This is not an helpful model in that (a) it does not explain anything, as it does not make use of covariates or side information, and (b) it has no predictive power, especially in the tails.  On the first point, concluding that Israel’s approach to counter-terrorism is successful because it “is the only country that has been able to bend” the power-law curve (p.442) sounds rather hasty. I’d like to see the same picture for Iraq, say. Actually, I found one in this arXiv paper. And it looks about the same for Afghanistan (Fig.4). On the second point, the modelling is poor in handling extreme values (which are the ones of interest in both cases) and cannot face change-points or lacks of stationary, an issue not sufficiently covered in s&n in my opinion. The difficulty with modelling volatile concepts like the stock market, the next presidential election or the move of your poker opponents is that there is no physical, immutable, law at play. Things can change from one instant to the next. Unpredictably. Esp. in the tails.

There are plenty of graphs in s&n, which is great, but not all of them are at the Tufte quality level. For instance, Figure 11-1 about the “average time U.S. common stock was held” contains six pie charts corresponding to six decades with the average time and a percentage which could be how long compared with the 1950s a stock was held. The graph is not mentioned in the text. (I will not mention Figure 8-2!) I also spotted a minuscule typo (`probabalistic’) on Figure 10-2A.

Maybe one last and highly personal remark about the chapter on poker (feel free to skip!): while I am a very poor card player, I do not mind playing cards (and loosing) with my kids. However, I simply do not understand the rationale of playing poker. If there is no money at stake, the game does not seem to make sense since every player can keep bluffing until the end of time. And if there is money at stake, I find the whole notion unethical. This is a zero sum game, so money comes from someone else’s pocket (or more likely someone else’s retirement plan or someone else’s kids college savings plan). Not much difference with the way the stock market behaves nowadays… (Incidentally, this chapter did not discuss at all the performances of computer poker programs, unexpectedly, as the number of possibilities is very small and they should thus be fairly efficient.)

L’Aquila: earthquake, verdict, and statistics

Posted in Statistics, University life with tags , , , , , , , , , on October 25, 2012 by xi'an

Yesterday I read this blog entry by Peter Coles, a Professor of Theoretical Astrophysics at Cardiff and soon in Brighton, about L’Aquila earthquake verdict, condemning six Italian scientists to severe jail sentences. While most of the blogs around reacted against this verdict as an anti-scientific decision and as a 21st Century remake of Giordano Bruno‘s murder by the Roman Inquisition, Peter Coles argues in the opposite that the scientists were not scientific enough in that instance. And should have used statistics and probabilistic reasoning. While I did not look into the details of the L’Aquila earthquake judgement and thus have no idea whether or not the scientists were guilty in not signalling the potential for disaster, were an earthquake to occur, I cannot but repost one of Coles’ most relevant paragraphs:

I thought I’d take this opportunity to repeat the reasons I think statistics and statistical reasoning are so important. Of course they are important in science. In fact, I think they lie at the very core of the scientific method, although I am still surprised how few practising scientists are comfortable even with statistical language. A more important problem is the popular impression that science is about facts and absolute truths. It isn’t. It’s a process. In order to advance, it has to question itself.

Statistical reasoning also applies outside science to many facets of everyday life, including business, commerce, transport, the media, and politics. It is a feature of everyday life that science and technology are deeply embedded in every aspect of what we do each day. Science has given us greater levels of comfort, better health care, and a plethora of labour-saving devices. It has also given us unprecedented ability to destroy the environment and each other, whether through accident or design. Probability even plays a role in personal relationships, though mostly at a subconscious level.

A bit further down, Peter Coles also bemoans the shortcuts and oversimplification of scientific journalism, which reminded me of the time Jean-Michel Marin had to deal with radio journalists about an “impossible” lottery coincidence:

Years ago I used to listen to radio interviews with scientists on the Today programme on BBC Radio 4. I even did such an interview once. It is a deeply frustrating experience. The scientist usually starts by explaining what the discovery is about in the way a scientist should, with careful statements of what is assumed, how the data is interpreted, and what other possible interpretations might be and the likely sources of error. The interviewer then loses patience and asks for a yes or no answer. The scientist tries to continue, but is badgered. Either the interview ends as a row, or the scientist ends up stating a grossly oversimplified version of the story.