NYC Meetup Thursday: Under the hood: Stan’s library, language, and algorithms

January 11, 2019
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(This article was first published on R – Statistical Modeling, Causal Inference, and Social Science, and kindly contributed to R-bloggers)

I (Bob, not Andrew!) will be doing a meetup talk this coming Thursday in New York City. Here’s the link with registration and location and time details (summary: pizza unboxing at 6:30 pm in SoHo):

After summarizing what Stan does, this talk will focus on how Stan is engineered. The talk follows the organization of the Stan software.

Stan math library: differentiable math and stats functions, template metaprorgrams to manage constants and vectorization, matrix derivatives, and differential equation derivatives.

Stan language: block structure and execution, unconstraining variable transforms and automatic Jacobians, transformed data, parameters, and generated quantities execution.

Stan algorithms: Hamiltonian Monte Carlo and the no-U-turn sampler (NUTS), automatic differentiation variational inference (ADVI).

Stan infrastructure and process: Time permitting, I can also discuss Stan’s developer process, how the code repositories are organized, and the code review and continuous integration process for getting new code into the repository

Slides

I realized I’m missing a good illustration of NUTS and how it achieves detailed balance and preferentially selects positions on the Hamiltonian trajectory toward the end of the simulated dynamics (to minimize autocorrelation in the draws). It was only an hour, so I skipped the autodiff section and scalable algorithms section and jumped to the end. I’ll volunteer do another meetup with the second half of the talk.

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