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Archive of posts tagged Stan

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 […]