andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1748 knowledge-graph by maker-knowledge-mining
Source: html
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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1 Stan is written in C++ and can be run from the command line and from R. [sent-1, score-0.523]
2 We’d like for Python users to be able to run Stan as well. [sent-2, score-0.632]
3 If anyone is interested in doing this, please let us know and we’d be happy to work with you on it. [sent-3, score-0.41]
4 Stan, like Python, is completely free and open-source. [sent-4, score-0.186]
5 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. [sent-7, score-0.682]
6 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. [sent-9, score-1.14]
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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: 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: Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool (aside from the Stan user doc)? What I’d like to understand is, at what point might you look at an emergent R project and advise, “You know, that thing you’re trying to do would be a whole lot easier/simpler/more straightforward to implement with Stan.” (or words to that effect). My reply: For my collaborators in political science, Stan has been most useful for models where the data set is not huge (e.g., we might have 10,000 data points or 50,000 data points but not 10 million) but where the model is somewhat complex (for example, a model with latent time series structure). The point is that the model has enough parameters and uncertainty that you’ll want to do full Bayes (rather than some sort
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Introduction: Richard McElreath writes: I’ve been translating a few ongoing data analysis projects into Stan code, mostly with success. The most important for me right now has been a hierarchical zero-inflated gamma problem. This a “hurdle” model, in which a bernoulli GLM produces zeros/nonzeros, and then a gamma GLM produces the nonzero values, using varying effects correlated with those in the bernoulli process. The data are 20 years of human foraging returns from a subsistence hunting population in Paraguay (the Ache), comprising about 15k hunts in total (Hill & Kintigh. 2009. Current Anthropology 50:369-377). Observed values are kilograms of meat returned to camp. The more complex models contain a 147-by-9 matrix of varying effects (147 unique hunters), as well as imputation of missing values. Originally, I had written the sampler myself in raw R code. It was very slow, but I knew what it was doing at least. Just before Stan version 1.0 was released, I had managed to get JAGS to do it a
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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: The Stan Development Team is happy to announce CmdStan, RStan, and PyStan v2.2.0. As usual, more info is available on the Stan Home Page . This is a minor release with a mix of bug fixes and features. For a full list of changes, please see the v2.2.0 milestone on stan-dev/stan’s issue tracker. Some of the bug fixes and issues are listed below. Bug Fixes increment_log_prob is now vectorized and compiles with vector arguments multinomial random number generator used the wrong size for the return value fixed memory leaks in auto-diff implementation variables can start with the prefix ‘inf’ fixed parameter output order for arrays when using optimization RStan compatibility issue with latest Rcpp 0.11.0 Features suppress command line output with refresh <= 0 added 1 to treedepth to match usual definition of treedepth added distance, squared_distance, diag_pre_multiply, diag_pre_multiply to Stan modeling lnaguage added a ‘fixed_param’ sampler for
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Introduction: Awhile ago I was cleaning out the closet and found some old unread magazines. Good stuff. As we’ve discussed before , lots of things are better read a few years late. Today I was reading the 18 Nov 2004 issue of the London Review of Books, which contained (among other things) the following: - A review by Jenny Diski of a biography of Stanley Milgram. Diski appears to want to debunk: Milgram was a whiz at devising sexy experiments, but barely interested in any theoretical basis for them. They all have the same instant attractiveness of style, and then an underlying emptiness. Huh? Michael Jordan couldn’t hit the curveball and he was reportedly an easy mark for golf hustlers but that doesn’t diminish his greatness on the basketball court. She also criticizes Milgram for being “no help at all” for solving international disputes. OK, fine. I haven’t solved any international disputes either. Milgram, though, . . . he conducted an imaginative experiment whose results stu
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Introduction: From two years ago : Awhile ago I was cleaning out the closet and found some old unread magazines. Good stuff. As we’ve discussed before , lots of things are better read a few years late. Today I was reading the 18 Nov 2004 issue of the London Review of Books, which contained (among other things) the following: - A review by Jenny Diski of a biography of Stanley Milgram. Diski appears to want to debunk: Milgram was a whiz at devising sexy experiments, but barely interested in any theoretical basis for them. They all have the same instant attractiveness of style, and then an underlying emptiness. Huh? Michael Jordan couldn’t hit the curveball and he was reportedly an easy mark for golf hustlers but that doesn’t diminish his greatness on the basketball court. She also criticizes Milgram for being “no help at all” for solving international disputes. OK, fine. I haven’t solved any international disputes either. Milgram, though, . . . he conducted an imaginative exp
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Introduction: Check out comment #9 here . All we need is for Steven Levitt, David Runciman, and some Reader in Management somewhere to weigh in and we’ll be all set.
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Introduction: There was some confusion on my last try , so let me explain one more time . . . The flights I where Hipmunk failed (see here for background) were not obscure itineraries. One of them was a nonstop from New York to Cincinnati; another was from NY to Durham, North Carolina; and yet another was a trip to Midway in Chicago. In that last case, Hipmunk showed no nonstops at all—which will come as a surprise to the passengers on the Southwest Airlines flight I was on a couple days ago! In these cases, Hipmunk didn’t even do the courtesy of flashing a message telling me to try elsewhere. I don’t understand. How hard would it be for the program to automatically do a Kayak search and find all the flights? Hipmunk’s graphics are great, though. Lee Wilkinson reports: Check out the figure below from The Grammar of Graphics. Dan Rope invented this graphic and programmed it in Java in the late 1990′s. We shopped this graph around to Orbitz and Expedia but they weren’t interested. So I
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