andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1131 knowledge-graph by maker-knowledge-mining
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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: Here’s Bob’s talk from the NYC machine learning meetup . And here’s Stan himself:
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Introduction: Thurs 19 Jan 7pm at the NYC Machine Learning meetup. Stan ‘s entirely publicly funded and open-source and it has no secrets . Ask us about it and we’ll tell you everything you might want to know. P.S. And here ‘s the talk.
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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: Following up on our previous post , Andrew Wilson writes: I agree we are in a really exciting time for statistics and machine learning. There has been a lot of talk lately comparing machine learning with statistics. I am curious whether you think there are many fundamental differences between the fields, or just superficial differences — different popular approximate inference methods, slightly different popular application areas, etc. Is machine learning a subset of statistics? In the paper we discuss how we think machine learning is fundamentally about pattern discovery, and ultimately, fully automating the learning and decision making process. In other words, whatever a human does when he or she uses tools to analyze data, can be written down algorithmically and automated on a computer. I am not sure if the ambitions are similar in statistics — and I don’t have any conventional statistics background, which makes it harder to tell. I think it’s an interesting discussion.
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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: Stan will make a total lifetime profit of $0, so we can’t be sued !
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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 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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