andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2124 knowledge-graph by maker-knowledge-mining
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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: 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: 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.
3 0.19932367 1475 andrew gelman stats-2012-08-30-A Stan is Born
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.
4 0.18478101 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
5 0.17153525 2209 andrew gelman stats-2014-02-13-CmdStan, RStan, PyStan v2.2.0
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: 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: 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: We have a Stan users meetup for NYC. We’ll have monthly sessions where we can discuss modeling, success stories, pain points, and really have a chance for the user base and the developers to interact in NYC. The first meetup will be on Tuesday, 5/13. I’ll be giving a overview of Stan aimed at a general audience. If you’re interested, please register for the group / talk. Space is limited.
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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: Jim Savage saw this and pointed me to this video. I didn’t actually look at it, but given that it is labeled, “For new econ Ph.D.’s about to look for a job . . . what you might expect when you give your first talk presenting your research,” I can pretty much guess what it’ll look like.
3 0.86773199 1261 andrew gelman stats-2012-04-12-The Naval Research Lab
Introduction: I worked at the U.S. Naval Research Laboratory for four summers during high school and college. I spent much of my time writing a computer program to do thermal analysis for an experiment that we put on the space shuttle. The facility I developed with the finite-element method came in handy in my job at Bell Labs the following summers. I was working for C. H. Tsao and Jim Adams in the Laboratory for Cosmic Ray Physics. We were estimating the distribution of isotopes in cosmic rays using a pile of track detectors. To get accurate measurements, you want these plastic disks to be as close as possible to a constant temperature, so we designed an elaborate wrapping of thermal blankets. My program computed the temperature of the detectors during the year that the Long Duration Exposure Facility (including our experiment and a bunch of others) was scheduled to be in orbit. The input is the heat from solar radiation (easy enough to compute given the trajectory). On the computer I tr
4 0.86202455 2275 andrew gelman stats-2014-03-31-Just gave a talk
Introduction: I just gave a talk in Milan. Actually I was sitting at my desk, it was a g+ hangout which was a bit more convenient for me. The audience was a bunch of astronomers so I figured they could handle a satellite link. . . . Anyway, the talk didn’t go so well. Two reasons: first, it’s just hard to get the connection with the audience without being able to see their faces. Next time I think I’ll try to get several people in the audience to open up their laptops and connect to the hangout, so that I can see a mosaic of faces instead of just a single image from the front of the room. The second problem with the talk was the topic. I asked the people who invited me to choose a topic, and they picked Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? But I don’t think this was right for this audience. I think that it would’ve been better to give them the Stan talk or the little data talk or the statistic
5 0.86195862 1668 andrew gelman stats-2013-01-11-My talk at the NY data visualization meetup this Monday!
Introduction: It’s in midtown at 7pm (on Mon 14 Jan 2013). Last time I talked for this group, I spoke on Infovis vs. Statistical Graphics . This time I plan to just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there will be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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