articleJan 1, 2017Closed access

Stan: A Probabilistic Programming Language

Abstract

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.2.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes,…

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Authors

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Topics & keywords

Keywords
  • Python (programming language)
  • Computer science
  • Markov chain Monte Carlo
  • Algorithm
  • Hybrid Monte Carlo
  • Monte Carlo method
  • Probabilistic logic
  • Bayesian inference
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