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2096 andrew gelman stats-2013-11-10-Schiminovich is on The Simpsons


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Introduction: OK, fine . Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?


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1 I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. [sent-3, score-1.541]

2 Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show? [sent-4, score-0.519]


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Introduction: OK, fine . Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?

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Introduction: Elsewhere: 1. They asked me to write about my “favorite election- or campaign-related movie, novel, or TV show” (Salon) 2. The shopping period is over; the time for buying has begun (NYT) 3. If anybody’s gonna be criticizing my tax plan, I want it to be this guy (Monkey Cage) 4. The 4 key qualifications to be a great president; unfortunately George W. Bush satisfies all four, and Ronald Reagan doesn’t match any of them (Monkey Cage) 5. The politics of eyeliner (Monkey Cage)

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Introduction: Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors. RStan is the R interface to Stan. Stan Home Page Stan’s home page is: http://mc-stan.org/ It links everything you need to get started running Stan from the command line, from R, or from C++, including full step-by-step install instructions, a detailed user’s guide and reference manual for the modeling language, and tested ports of most of the BUGS examples. Peruse the Manual If you’d like to learn more, the Stan User’s Guide and Reference Manual is the place to start.

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Introduction: OK, so this sort of thing happens sometimes. I was working on a new idea (still working on it; if it ultimately works out—or if it doesn’t—I’ll let you know) and as part of it I was fitting little models in Stan, in a loop. I thought it would make sense to start with linear regression with normal priors and known data variance, because then the exact solution is Gaussian and I can also work with the problem analytically. So I programmed up the algorithm and, no surprise, it didn’t work. I went through my R code, put in print statements here and there, and cleared out bug after bug until at least it stopped crashing. But the algorithm still wasn’t doing what it was supposed to do. So I decided to do something simpler, and just check that the Stan linear regression gave the same answer as the analytic posterior distribution: I ran Stan for tons of iterations, then computed the sample mean and variance of the simulations. It was an example with two coefficients—I’d originally cho

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Introduction: Stan is written in C++ and can be run from the command line and from R. We’d like for Python users to be able to run Stan as well. If anyone is interested in doing this, please let us know and we’d be happy to work with you on it. Stan, like Python, is completely free and open-source. P.S. Because Stan is open-source, it of course would also be possible for people to translate Stan into Python, or to take whatever features they like from Stan and incorporate them into a Python package. That’s fine too. But we think it would make sense in addition for users to be able to run Stan directly from Python, in the same way that it can be run from R.

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Introduction: OK, fine . Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?

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Introduction: Stan will make a total lifetime profit of $0, so we can’t be sued !

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Introduction: Stan is alive and well. We’re up to 523 people on the users list . [We're sure there are many more than 523 actual users, since it's easy to download and use Stan directly without joining the list.] We’re working on a v2.1.0 release now and we hope to release it in within the next couple of weeks.

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Introduction: OK, fine . Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?

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Introduction: Steve Ziliak points me to this article by the always-excellent Carl Bialik, slamming hypothesis tests. I only wish Carl had talked with me before so hastily posting, though! I would’ve argued with some of the things in the article. In particular, he writes: Reese and Brad Carlin . . . suggest that Bayesian statistics are a better alternative, because they tackle the probability that the hypothesis is true head-on, and incorporate prior knowledge about the variables involved. Brad Carlin does great work in theory, methods, and applications, and I like the bit about the prior knowledge (although I might prefer the more general phrase “additional information”), but I hate that quote! My quick response is that the hypothesis of zero effect is almost never true! The problem with the significance testing framework–Bayesian or otherwise–is in the obsession with the possibility of an exact zero effect. The real concern is not with zero, it’s with claiming a positive effect whe

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