andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-357 knowledge-graph by maker-knowledge-mining
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Introduction: Xian sends along this link that might be of interest to some of you.
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same-blog 1 0.99999994 357 andrew gelman stats-2010-10-20-Sas and R
Introduction: Xian sends along this link that might be of interest to some of you.
2 0.30202532 467 andrew gelman stats-2010-12-14-Do we need an integrated Bayesian-likelihood inference?
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).
3 0.28980541 1359 andrew gelman stats-2012-06-02-Another retraction
Introduction: Xian points me to this pitiful story. I hate that these people never just say they’re sorry, for wasting everyone’s time if for nothing else.
4 0.24777123 290 andrew gelman stats-2010-09-22-Data Thief
Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?
5 0.17877688 869 andrew gelman stats-2011-08-24-Mister P in Stata
Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
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20 0.11387738 306 andrew gelman stats-2010-09-29-Statistics and the end of time
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same-blog 1 0.9925735 357 andrew gelman stats-2010-10-20-Sas and R
Introduction: Xian sends along this link that might be of interest to some of you.
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Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?
3 0.71895951 587 andrew gelman stats-2011-02-24-5 seconds of every #1 pop single
Introduction: This is pretty amazing. Now I want to hear volume 3. Also is there a way to download this as I play it so I can listen when I’m offline? P.S. Typo in title fixed. P.P.S. I originally gave a different link but was led to the apparently more definitive link above (which allows direct download) from a commenter . Thanks!
4 0.70376903 1433 andrew gelman stats-2012-07-28-LOL without the CATS
Introduction: Mayo points me to this discussion [link fixed] on parsimony by philosopher Elliott Sober. I don’t really understand what he’s talking about but I am posting the link here because it might interest some of you. P.S. More discussion on this from Mayo here .
5 0.7008031 1257 andrew gelman stats-2012-04-10-Statisticians’ abbreviations are even less interesting than these!
Introduction: From AC, AI, and AIH to WAHM, WOHM, and WM. P.S. That was all pretty pointless, so I’ll throw in this viral Jim Henson link (from the same source) for free.
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same-blog 1 0.92402005 357 andrew gelman stats-2010-10-20-Sas and R
Introduction: Xian sends along this link that might be of interest to some of you.
2 0.85047847 335 andrew gelman stats-2010-10-11-How to think about Lou Dobbs
Introduction: I was unsurprised to read that Lou Dobbs, the former CNN host who crusaded against illegal immigrants, had actually hired a bunch of them himself to maintain his large house and his horse farm. (OK, I have to admit I was surprised by the part about the horse farm.) But I think most of the reactions to this story missed the point. Isabel Macdonald’s article that broke the story was entitled, “Lou Dobbs, American Hypocrite,” and most of the discussion went from there, with some commenters piling on Dobbs and others defending him by saying that Dobbs hired his laborers through contractors and may not have known they were in the country illegally. To me, though, the key issue is slightly different. And Macdonald’s story is relevant whether or not Dobbs knew he was hiring illegals. My point is not that Dobbs is a bad guy, or a hypocrite, or whatever. My point is that, in his setting, it would take an extraordinary effort to not hire illegal immigrants to take care of his house
3 0.84735674 940 andrew gelman stats-2011-10-03-It depends upon what the meaning of the word “firm” is.
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.
4 0.84133351 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!
5 0.82831395 1749 andrew gelman stats-2013-03-04-Stan in L.A. this Wed 3:30pm
Introduction: Michael Betancourt will be speaking at UCLA: The location for refreshment is in room 51-254 CHS at 3:00 PM. The place for the seminar is at CHS 33-105A at 3:30pm – 4:30pm, Wed 6 Mar. ["CHS" stands for Center for Health Sciences, the building of the UCLA schools of medicine and public health. Here's a map with directions .] 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.
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