andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1809 knowledge-graph by maker-knowledge-mining
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Introduction: Xi’an’s Og (aka Christian Robert’s blog) is featuring a very nice presentation of NUTS by Marco Banterle, with discussion and some suggestions. I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link.
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2 I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link. [sent-2, score-0.783]
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same-blog 1 1.0 1809 andrew gelman stats-2013-04-17-NUTS discussed on Xi’an’s Og
Introduction: Xi’an’s Og (aka Christian Robert’s blog) is featuring a very nice presentation of NUTS by Marco Banterle, with discussion and some suggestions. I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link.
2 0.21920849 2114 andrew gelman stats-2013-11-26-“Please make fun of this claim”
Introduction: Jeff sent me an email with the above title and a link to a press release, “Nut consumption reduces risk of death,” which begins: According to the largest study of its kind, people who ate a daily handful of nuts were 20 percent less likely to die from any cause over a 30-year period than those who didn’t consume nuts . . . Their report, published in the New England Journal of Medicine, contains further good news: The regular nut-eaters were found to be more slender than those who didn’t eat nuts, a finding that should alleviate fears that eating a lot of nuts will lead to overweight. . . . For the new research, the scientists were able to tap databases from two well-known, ongoing observational studies that collect data on diet and other lifestyle factors and various health outcomes. The Nurses’ Health Study provided data on 76,464 women between 1980 and 2010, and the Health Professionals’ Follow-Up Study yielded data on 42,498 men from 1986 to 2010. . . . Sophisticated data a
3 0.17835897 1036 andrew gelman stats-2011-11-30-Stan uses Nuts!
Introduction: We interrupt our usual program of Ed Wegman Gregg Easterbrook Niall Ferguson mockery to deliver a serious update on our statistical computing project. Stan (“Sampling Through Adaptive Neighborhoods”) is our new C++ program (written mostly by Bob Carpenter) that draws samples from Bayesian models. Stan can take different sorts of inputs: you can write the model in a Bugs-like syntax and it goes from there, or you can write the log-posterior directly as a C++ function. Most of the computation is done using Hamiltonian Monte Carlo. HMC requires some tuning, so Matt Hoffman up and wrote a new algorithm, Nuts (the “No-U-Turn Sampler”) which optimizes HMC adaptively. In many settings, Nuts is actually more computationally efficient than the optimal static HMC! When the the Nuts paper appeared on Arxiv, Christian Robert noticed it and had some reactions . In response to Xian’s comments, Matt writes: Christian writes: I wonder about the computing time (and the “una
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Introduction: Xiaoyu Qian writes: I have a question when I apply the half-Cauchy prior (Gelman, 2006) for the variance parameter in a hierarchical model. The model I used is a three level IRT model equivalent to a Rasch model. The variance parameter I try to estimate is at the third level. The group size ranges from 15 to 44. The data is TIMSS 2007 data. I used the syntax provided by the paper and found that the convergence of the standard deviation term is good (sigma.theta), however, the convergence for the parameter “xi” is not very good. Does it mean the whole model has not converged? Do you have any suggestion for this situation. I also used the uniform prior and correlate the result with the half-Cauchy result for the standard deviation term. The results correlated .99. My reply: It’s not a problem if xi does not converge well. It’s |xi|*sigma that is relevant. And, if the number of groups is large, the prior probably won’t matter so much, which would explain your 99% correlat
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Introduction: Wayne Folta sends in this . It seems nuts to me (although I was happy to see that no mention was made of this horrible argument of a related sort). But I know nothing about theoretical physics so I suppose it’s all possible. I certainly have no sense of confidence in anything I’d say about the topic.
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Introduction: Xi’an’s Og (aka Christian Robert’s blog) is featuring a very nice presentation of NUTS by Marco Banterle, with discussion and some suggestions. I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link.
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Introduction: Mike Spagat writes: Here is yet another debunking article I’ve written, this one in the latest issue of Significance. It shows the Lancet once again publishing spectacularly wrong information that has misinformed public discussion on a crucial issue with ongoing reverberations. For example, there is Tony Blair’s recent justification for the Iraq war offered in front of the Chilcot Inquiry in the UK quoted at the beginning of the paper. I haven’t had a chance to look at this one, but here’s a link to some related Spagat work.
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Introduction: I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion.
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Introduction: Martyn Plummer , the creator of the open-source, C++, graphical-model compiler JAGS (aka “Just Another Gibbs Sampler”), runs a forum on the JAGS site that has a very similar feel to the mail-bag posts on this blog. Martyn answers general statistical computing questions (e.g., why slice sampling rather than Metropolis-Hastings?) and general modeling (e.g., why won’t my model converge with this prior?). Here’s the link to the top-level JAGS site, and to the forum: JAGS Forum JAGS Home Page The forum’s pretty active, with the stats page showing hundreds of views per day and very regular posts and answers. Martyn’s last post was today. Martyn also has a blog devoted to JAGS and other stats news: JAGS News Blog
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Introduction: Mike Betancourt sends along this paper . Could be interesting, no? Note the heavy tail on the CDF in Figure 3, exhibiting weakened median time since 1999. And, as you can see from the bibliography, the work draws on a variety of sources:
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Introduction: Xi’an’s Og (aka Christian Robert’s blog) is featuring a very nice presentation of NUTS by Marco Banterle, with discussion and some suggestions. I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link.
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Introduction: Recently in the sister blog . Yet another chapter in the continuing saga, Don’t Trust Polynomials. P.S. More here .
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Introduction: In case you haven’t been following: - Top ten excuses for plagiarism - Why I won’t be sad to see Anthony Weiner retire - U.S. voter participation has not fallen steadily over the past few decades - Scott Adams had an interesting idea
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Introduction: A moviegoing colleague writes: I just watched the movie Zero Dark Thirty about the hunt for Osama Bin Laden. What struck me about it was: (1) Bayes theorem underlies the whole movie; (2) CIA top brass do not know Bayes theorem (at least as portrayed in the movie). Obviously one does not need to know physics to play billiards, but it helps with the reasoning. Essentially, at some point the key CIA agent locates what she strongly believes is OBL’s hidding place in Pakistan. Then it takes the White House some 150 days to make the decision to attack the compound. Why so long? And why, even on the eve of the operation, were senior brass only some 60% OBL was there? Fear of false positives is the answer. After all, the compound could belong to a drug lord, or some other terrorist. Here is the math: There are two possibilities, according to movie: OBL is in a compound (C) in a city or he is in the mountains in tribal regions. Say P(OBL in C) = 0.5. A diagnosis is made on
Introduction: Following up on our recent discussion of visually-weighted displays of uncertainty in regression curves, Lucas Leeman sent in the following two graphs: First, the basic spaghetti-style plot showing inferential uncertainty in the E(y|x) curve: Then, a version using even lighter intensities for the lines that go further from the center, to further de-emphasize the edges: P.S. More (including code!) here .
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