andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2035 knowledge-graph by maker-knowledge-mining
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Introduction: Bob writes: If you have papers that have used Stan, we’d love to hear about it. We finally got some submissions, so we’re going to start a list on the web site for 2.0 in earnest. You can either mail them to the list, to me directly, or just update the issue (at least until it’s closed or moved): https://github.com/stan-dev/stan/issues/187 For example, Henrik Mannerstrom fit a hierarchical model the other day with 360,000 data points and 120,000 variables. And it worked just fine in Stan. I’ve asked him to write this up so we can post it here. Here’s the famous graph Bob made showing the scalability of Stan for a series of hierarchical item-response models:
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1 Bob writes: If you have papers that have used Stan, we’d love to hear about it. [sent-1, score-0.424]
2 We finally got some submissions, so we’re going to start a list on the web site for 2. [sent-2, score-0.901]
3 You can either mail them to the list, to me directly, or just update the issue (at least until it’s closed or moved): https://github. [sent-4, score-0.848]
4 com/stan-dev/stan/issues/187 For example, Henrik Mannerstrom fit a hierarchical model the other day with 360,000 data points and 120,000 variables. [sent-5, score-0.619]
5 I’ve asked him to write this up so we can post it here. [sent-7, score-0.265]
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Introduction: Bob writes: If you have papers that have used Stan, we’d love to hear about it. We finally got some submissions, so we’re going to start a list on the web site for 2.0 in earnest. You can either mail them to the list, to me directly, or just update the issue (at least until it’s closed or moved): https://github.com/stan-dev/stan/issues/187 For example, Henrik Mannerstrom fit a hierarchical model the other day with 360,000 data points and 120,000 variables. And it worked just fine in Stan. I’ve asked him to write this up so we can post it here. Here’s the famous graph Bob made showing the scalability of Stan for a series of hierarchical item-response models:
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Introduction: Here’s Bob’s talk from the NYC machine learning meetup . And here’s Stan himself:
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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: 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.
5 0.15280846 2150 andrew gelman stats-2013-12-27-(R-Py-Cmd)Stan 2.1.0
Introduction: We’re happy to announce the release of Stan C++, CmdStan, RStan, and PyStan 2.1.0. This is a minor feature release, but it is also an important bug fix release. As always, the place to start is the (all new) Stan web pages: http://mc-stan.org Major Bug in 2.0.0, 2.0.1 Stan 2.0.0 and Stan 2.0.1 introduced a bug in the implementation of the NUTS criterion that led to poor tail exploration and thus biased the posterior uncertainty downward. There was no bug in NUTS in Stan 1.3 or earlier, and 2.1 has been extensively tested and tests put in place so this problem will not recur. If you are using Stan 2.0.0 or 2.0.1, you should switch to 2.1.0 as soon as possible and rerun any models you care about. New Target Acceptance Rate Default for Stan 2.1.0 Another big change aimed at reducing posterior estimation bias was an increase in the target acceptance rate during adaptation from 0.65 to 0.80. The bad news is that iterations will take around 50% longer
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Introduction: Shravan Vasishth sent me an earlier draft of this tutorial he co-authored with Tanner Sorensen. I liked it, asked if I could blog about it, and in response, they’ve put together a convenient web page with links to the tutorial PDF, JAGS and Stan programs, and data: Fitting linear mixed models using JAGS and Stan: A tutorial The tutorial’s aimed at psycholinguists in particular and cognitive psychologists more generally, but it’s clear enough to be understood by anyone. Plus, the psycholinguistic data is a lot of fun. There’s contact info on the tutorial itself if you want to send the authors feedback directly. I have to say I’m very psyched to see other people writing tutorials for Stan for particular application areas of interest.
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Introduction: If you need an excuse to go skiing in Tahoe next month, our paper on Stan as a probabilistic programming language was accepted for: Workshop on Probabilistic Programming NIPS 2012 7–8 December, 2012, Lake Tahoe, Nevada The workshop is organized by the folks behind the probabilistic programming language Church and has a great lineup of invited speakers (Chris Bishop, Josh Tennenbaum, and Stuart Russell). And in case you’re interested in the main conference, here’s the list of accepted NIPS 2012 papers and posters . To learn more about Stan, check out the links to the manual on the Stan Home Page We’ll put up a link to our final NIPS workshop paper there when we finish it.
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Introduction: Bob writes: If you have papers that have used Stan, we’d love to hear about it. We finally got some submissions, so we’re going to start a list on the web site for 2.0 in earnest. You can either mail them to the list, to me directly, or just update the issue (at least until it’s closed or moved): https://github.com/stan-dev/stan/issues/187 For example, Henrik Mannerstrom fit a hierarchical model the other day with 360,000 data points and 120,000 variables. And it worked just fine in Stan. I’ve asked him to write this up so we can post it here. Here’s the famous graph Bob made showing the scalability of Stan for a series of hierarchical item-response models:
Introduction: Type S error: When your estimate is the wrong sign, compared to the true value of the parameter Type M error: When the magnitude of your estimate is far off, compared to the true value of the parameter More here.
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Introduction: After seeing a document sent to me and others regarding the crisis of spurious, statistically-significant research findings in psychology research, I had the following reaction: I am unhappy with the use in the document of the phrase “false positives.” I feel that this expression is unhelpful as it frames science in terms of “true” and “false” claims, which I don’t think is particularly accurate. In particular, in most of the recent disputed Psych Science type studies (the ESP study excepted, perhaps), there is little doubt that there is _some_ underlying effect. The issue, as I see it, as that the underlying effects are much smaller, and much more variable, than mainstream researchers imagine. So what happens is that Psych Science or Nature or whatever will publish a result that is purported to be some sort of universal truth, but it is actually a pattern specific to one data set, one population, and one experimental condition. In a sense, yes, these journals are publishing
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