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Archive of posts tagged Stan

StanCon: now accepting registrations and submissions

As we announced here a few weeks ago, the first Stan conference will be Saturday, January 21, 2017 at Columbia University in New York. We are now accepting both conference registrations and submissions. Full details are available at StanCon page on the Stan website. If you have any questions please let us know and we […]

StanCon is coming! Sat, 1/21/2017

Save the date! The first Stan conference is going to be in NYC in January. Registration will open at the end of September.   When: Saturday, January 21, 2017 9 am – 5 pm   Where: Davis Auditorium, Columbia University 530 West 120th Street 4th floor (campus level), room 412 New York, NY 10027   […]

NYC Stan meetup 12 December

The next NYC Stan meetup is on Saturday: Feel free to bring things you’re working on or join in on projects some of the others are working on. A couple of the developers will be around to answer questions and help out. If you don’t have anything to work on, the Stan team could use […]

Daniel on Stan at the NYC Machine Learning Meetup

I (Daniel) will be giving a Stan overview talk on Thursday, August 20, 7 pm. Bob gave a talk there 3.5 years ago. My talk will be light and include where we’ve been and where we’re going.   P.S. If you make it, find me. I have Stan stickers to give out. P.P.S. Stan is […]

ShinyStan v2.0.0

For those of you not familiar with ShinyStan, it is a graphical user interface for exploring Stan models (and more generally MCMC output from any software). For context, here’s the post on this blog first introducing ShinyStan (formerly shinyStan) from earlier this year. ShinyStan v2.0.0 released ShinyStan v2.0.0 is now available on CRAN. This is […]

Stan is fast

10,000 iterations for 4 chains on the (precompiled) efficiently-parameterized 8-schools model:

A Stan is Born

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 […]

Learning Differential Geometry for Hamiltonian Monte Carlo

You can get a taste of Hamiltonian Monte Carlo (HMC) by reading the very gentle introduction in David MacKay’s general text on information theory: MacKay, D. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge University Press. [see Chapter 31, which is relatively standalone and can be downloaded separately.] Follow this up with Radford Neal’s much […]