andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-193 knowledge-graph by maker-knowledge-mining
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Introduction: Xian posts his memories of Julian Besag, who is perhaps most famous for publishing the Hammersley-Clifford theorem (see here for some background). I met Besag in 1989 when I spoke at the University of Washington; also I have a memory of a conference in 1992 or 1993, I think it was, when he objected strongly to my use of the chi-squared test to check the fit of a Bayesian model. (In retrospect, I don’t think I presented my ideas clearly enough; some of the material in that talk ended up in this article with Meng and Stern.) I also recall a talk I gave in Seattle around 1996, when Besag commented that the models I was using for spatial analysis were pretty crude–a fair comment, actually. I don’t actually recall any of Besag’s lectures, but I read many of his papers. As Xian said, Besag did innovative and important work on spatial statistics, work that will be long used and remembered. He was in many ways ahead of his time and was a rarity in his generation of Bayesian statist
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1 Xian posts his memories of Julian Besag, who is perhaps most famous for publishing the Hammersley-Clifford theorem (see here for some background). [sent-1, score-0.429]
2 I met Besag in 1989 when I spoke at the University of Washington; also I have a memory of a conference in 1992 or 1993, I think it was, when he objected strongly to my use of the chi-squared test to check the fit of a Bayesian model. [sent-2, score-0.749]
3 (In retrospect, I don’t think I presented my ideas clearly enough; some of the material in that talk ended up in this article with Meng and Stern. [sent-3, score-0.44]
4 ) I also recall a talk I gave in Seattle around 1996, when Besag commented that the models I was using for spatial analysis were pretty crude–a fair comment, actually. [sent-4, score-0.754]
5 I don’t actually recall any of Besag’s lectures, but I read many of his papers. [sent-5, score-0.193]
6 As Xian said, Besag did innovative and important work on spatial statistics, work that will be long used and remembered. [sent-6, score-0.408]
7 He was in many ways ahead of his time and was a rarity in his generation of Bayesian statisticians in being motivated by models and applications rather than by theory. [sent-7, score-0.712]
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Introduction: Xian posts his memories of Julian Besag, who is perhaps most famous for publishing the Hammersley-Clifford theorem (see here for some background). I met Besag in 1989 when I spoke at the University of Washington; also I have a memory of a conference in 1992 or 1993, I think it was, when he objected strongly to my use of the chi-squared test to check the fit of a Bayesian model. (In retrospect, I don’t think I presented my ideas clearly enough; some of the material in that talk ended up in this article with Meng and Stern.) I also recall a talk I gave in Seattle around 1996, when Besag commented that the models I was using for spatial analysis were pretty crude–a fair comment, actually. I don’t actually recall any of Besag’s lectures, but I read many of his papers. As Xian said, Besag did innovative and important work on spatial statistics, work that will be long used and remembered. He was in many ways ahead of his time and was a rarity in his generation of Bayesian statist
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Introduction: David Blackwell . Julian Besag . Arnold Zellner . Benoit Mandelbrot and Hirotugu Akaike (late) . Alfred Kahn .
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Introduction: Xian sends along this link that might be of interest to some of you.
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Introduction: The other day I came across a paper that referred to Charlie Geyer’s 1991 paper, “Estimating Normalizing Constants and Reweighting Mixtures in Markov Chain Monte Carlo.” I expect that part or all of this influential article was included in some published paper, but I only know it as a technical report–which at the time of this writing has been cited an impressive 78 times! This made me wonder: what are the most influential contributions to statistics that were never published (not counting posthumous publication or decades-later reprints in compilation volumes). Here’s all that I can think of: - Thomas Bayes’s original article, published only in 1763, two years after his death. - John Tukey’s legendary manuscript on multiple comparisons from the 1950s. I actually think Tukey’s work on multiple comparisons was horribly illogical stuff, very clever but also bad bad bad, and I’m very happy that he moved on to other things. But I can’t deny that his unpublished multiple compa
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Introduction: My recent article with Xian and Judith. In English. Interested readers can try to figure out which parts were written by each of the three authors (recognizing that each of us edited the whole thing).
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Introduction: Xian posts his memories of Julian Besag, who is perhaps most famous for publishing the Hammersley-Clifford theorem (see here for some background). I met Besag in 1989 when I spoke at the University of Washington; also I have a memory of a conference in 1992 or 1993, I think it was, when he objected strongly to my use of the chi-squared test to check the fit of a Bayesian model. (In retrospect, I don’t think I presented my ideas clearly enough; some of the material in that talk ended up in this article with Meng and Stern.) I also recall a talk I gave in Seattle around 1996, when Besag commented that the models I was using for spatial analysis were pretty crude–a fair comment, actually. I don’t actually recall any of Besag’s lectures, but I read many of his papers. As Xian said, Besag did innovative and important work on spatial statistics, work that will be long used and remembered. He was in many ways ahead of his time and was a rarity in his generation of Bayesian statist
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Introduction: Steve Ziliak reports: I [Ziliak] am sorry to share this sad news about Arnold Zellner (AEA Distinguished Fellow, 2002, ASA President, 1991, ISBA co-founding president, all around genius and sweet fellow), who died yesterday morning (August 11, 2010). He was a truly great statistician and to me and to many others a generous and wonderful friend, colleague, and hero. I will miss him. His cancer was spreading everywhere though you wouldn’t know it as his energy level was Arnold’s typical: abnormally high. But then he had a stroke just a few days after an unsuccessful surgery “to help with breathing” the doctor said, and the combination of events weakened him terribly. He was vibrant through June and much of July, and maintained an 8 hour work day at the office. He never lost his sense of humor nor his joy of life. He died at home in hospice care and fortunately he did not suffer long. From the official announcement from the University of Chicago: Arnold began his academi
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Introduction: From 2006 : Eric Archer forwarded this document by Nick Freemantle, “The Reverend Bayes—was he really a prophet?”, in the Journal of the Royal Society of Medicine: Does [Bayes's] contribution merit the enthusiasms of his followers? Or is his legacy overhyped? . . . First, Bayesians appear to have an absolute right to disapprove of any conventional approach in statistics without offering a workable alternative—for example, a colleague recently stated at a meeting that ‘. . . it is OK to have multiple comparisons because Bayesians’ don’t believe in alpha spending’. . . . Second, Bayesians appear to build an army of straw men—everything it seems is different and better from a Bayesian perspective, although many of the concepts seem remarkably familiar. For example, a very well known Bayesian statistician recently surprised the audience with his discovery of the P value as a useful Bayesian statistic at a meeting in Birmingham. Third, Bayesians possess enormous enthusiasm fo
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Introduction: David Blackwell was already retired by the time I came to Berkeley, and probably our closest connection was that I taught the class in decision theory that he used to teach. I enjoyed that class a lot, partly because it took me out of my usual comfort zone of statistical inference and data analysis toward something more theoretical and mathematical. Blackwell was one of the legendary figures in the department at that time and was also one of the most tolerant of alternative approaches to statistics, perhaps because of combination of a mathematical background, applied research in the war and after (which I learned about in this recent obituary ), and personal experiences, Blackwell may be best known in statistics for the Rao-Blackwell theorem . Rao, of course, is also famoust for the Cramer-Rao lower bound. Both theorems relate to minimum-variance statistical estimators. Here’s a quote from Thomas (Jesus’s dad) Ferguson in Blackwell’s obituary : He went from one area to an
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Introduction: The other day someone mentioned my complaint about the Wikipedia article on “Bayesian inference” (see footnote 1 of this article ) and he said I should fix the Wikipedia entry myself. And so I did . I didn’t have the energy to rewrite the whole article–in particular, all of its examples involve discrete parameters, whereas the Bayesian problems I work on generally have continuous parameters, and its “mathematical foundations” section focuses on “independent identically distributed observations x” rather than data y which can have different distributions. It’s just a wacky, unbalanced article. But I altered the first few paragraphs to get rid of the stuff about the posterior probability that a model is true. I much prefer the Scholarpedia article on Bayesian statistics by David Spiegelhalter and Kenneth Rice, but I couldn’t bring myself to simply delete the Wikipedia article and replace it with the Scholarpedia content. Just to be clear: I’m not at all trying to disparage
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1 0.93764699 1772 andrew gelman stats-2013-03-20-Stan at Google this Thurs and at Berkeley this Fri noon
Introduction: Michael Betancourt will be speaking at Google and at the University of California, Berkeley. The Google talk is closed to outsiders (but if you work at Google, you should go!); the Berkeley talk is open to all: Friday March 22, 12:10 pm, Evans Hall 1011. Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff. And he’ll be showing the whirlpool movie!
2 0.93201673 178 andrew gelman stats-2010-08-03-(Partisan) visualization of health care legislation
Introduction: Congressman Kevin Brady from Texas distributes this visualization of reformed health care in the US (click for a bigger picture): Here’s a PDF at Brady’s page, and a local copy of it. Complexity has its costs. Beyond the cost of writing it, learning it, following it, there’s also the cost of checking it. John Walker has some funny examples of what’s hidden in the almost 8000 pages of IRS code. Text mining and applied statistics will solve all that, hopefully. Anyone interested in developing a pork detection system for the legislation? Or an analysis of how much entropy to the legal code did each congressman contribute? There are already spin detectors , that help you detect whether the writer is a Democrat (“stimulus”, “health care”) or a Republican (“deficit spending”, “ObamaCare”). D+0.1: Jared Lander points to versions by Rep. Boehner and Robert Palmer .
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Introduction: Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how bad things can be at school, on a day-by-day basis.
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Introduction: David Hogg pointed me to this news article by Angela Saini: It’s not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law. At its heart, this is a story about chance. And it begins with a convicted killer, “T”, who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed. . . . “The impact will be quite shattering,” says Professor Norman Fenton, a mathematician at Queen Mary, University of London.
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