andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1660 knowledge-graph by maker-knowledge-mining
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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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same-blog 1 1.0 1660 andrew gelman stats-2013-01-08-Bayesian, Permutable Symmetries
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: Mike Jordan sends along this National Academies report on “big data.” This is not a research report but it could be interesting in that it conveys what are believed to be important technical challenges.
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Introduction: Xian sends along this link that might be of interest to some of you.
Introduction: Paul Pudaite writes in response to my discussion with Bartels regarding effect sizes and measurement error models: You [Gelman] wrote: “I actually think there will be some (non-Gaussian) models for which, as y gets larger, E(x|y) can actually go back toward zero.” I [Pudaite] encountered this phenomenon some time in the ’90s. See this graph which shows the conditional expectation of X given Z, when Z = X + Y and the probability density functions of X and Y are, respectively, exp(-x^2) and 1/(y^2+1) (times appropriate constants). As the magnitude of Z increases, E[X|Z] shrinks to zero. I wasn’t sure it was worth the effort to try to publish a two paragraph paper. I suspect that this is true whenever the tail of one distribution is ‘sufficiently heavy’ with respect to the tail of the other. Hmm, I suppose there might be enough substance in a paper that attempted to characterize this outcome for, say, unimodal symmetric distributions. Maybe someone can do this? I think i
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Introduction: Ido Rosen pointed me to this page by Mike Kamermans.
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same-blog 1 0.92937791 1660 andrew gelman stats-2013-01-08-Bayesian, Permutable Symmetries
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: Mike Spagat sends along a serious presentation with an ironic title: 18.7 MILLION ANNIHILATED SAYS LEADING EXPERT IN PEER–REVIEWED JOURNAL: AN APPROVED, AUTHORITATIVE, SCIENTIFIC PRESENTATION MADE BY AN EXPERT He’ll be speaking on it at tomorrow’s meeting of the Catastrophes and Conflict Forum of the Royal Society of Medicine in London. All I can say is, it’s a long time since I’ve seen a slide presentation in portrait form. It brings me back to the days of transparency sheets.
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Introduction: Ido Rosen pointed me to this page by Mike Kamermans.
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Introduction: Mike Jordan sends along this National Academies report on “big data.” This is not a research report but it could be interesting in that it conveys what are believed to be important technical challenges.
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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 0.87963206 1660 andrew gelman stats-2013-01-08-Bayesian, Permutable Symmetries
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: Data analysis recipes: Fitting a model to data : We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane. Standard weighted least-squares fitting is only appropriate when there is a dimension along which the data points have negligible uncertainties, and another along which all the uncertainties can be described by Gaussians of known variance; these conditions are rarely met in practice. We consider cases of general, heterogeneous, and arbitrarily covariant two-dimensional uncertainties, and situations in which there are bad data (large outliers), unknown uncertainties, and unknown but expected intrinsic scatter in the linear relationship being fit. Above all we emphasize the importance of having a “generative model” for the data, even an approximate one. Once there is a generative model, the subsequent fitting is non-arbitrary because the model permits direct computation
Introduction: A tall thin young man came to my office today to talk about one of my current pet topics: stories and social science. I brought up Tom Wolfe and his goal of compressing an entire city into a single novel, and how this reminded me of the psychologists Kahneman and Tversky’s concept of “the law of small numbers,” the idea that we expect any small sample to replicate all the properties of the larger population that it represents. Strictly speaking, the law of small numbers is impossible—any small sample necessarily has its own unique features—but this is even more true if we consider network properties. The average American knows about 700 people (depending on how you define “know”) and this defines a social network over the population. Now suppose you look at a few hundred people and all their connections. This mini-network will almost necessarily look much much sparser than the national network, as we’re removing the connections to the people not in the sample. Now consider how
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Introduction: Steve Hsu, who started off this discussion, had some comments on my speculations on the personality of John von Neumann and others. Steve writes: I [Hsu] actually knew Feynman a bit when I was an undergrad, and found him to be very nice to students. Since then I have heard quite a few stories from people in theoretical physics which emphasize his nastier side, and I think in the end he was quite a complicated person like everyone else. There are a couple of pseudo-biographies of vN, but none as high quality as, e.g., Gleick’s book on Feynman or Hodges book about Turing. (Gleick studied physics as an undergrad at Harvard, and Hodges is a PhD in mathematical physics — pretty rare backgrounds for biographers!) For example, as mentioned on the comment thread to your post, Steve Heims wrote a book about both vN and Wiener (!), and Norman Macrae wrote a biography of vN. Both books are worth reading, but I think neither really do him justice. The breadth of vN’s work is just too m
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Introduction: Macartan Humphreys pointed me to this excellent guide . Here are the 10 items: 1. A causal claim is a statement about what didn’t happen. 2. There is a fundamental problem of causal inference. 3. You can estimate average causal effects even if you cannot observe any individual causal effects. 4. If you know that, on average, A causes B and that B causes C, this does not mean that you know that A causes C. 5. The counterfactual model is all about contribution, not attribution. 6. X can cause Y even if there is no “causal path” connecting X and Y. 7. Correlation is not causation. 8. X can cause Y even if X is not a necessary condition or a sufficient condition for Y. 9. Estimating average causal effects does not require that treatment and control groups are identical. 10. There is no causation without manipulation. The article follows with crisp discussions of each point. My favorite is item #6, not because it’s the most important but because it brings in some real s
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