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Archive of posts tagged Stan

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 […]