Stan (software)

Stan
Original author(s)Stan Development Team
Initial releaseAugust 30, 2012 (2012-08-30)
Stable release
2.34.0 Edit this on Wikidata / 16 January 2024; 33 days ago (16 January 2024)
Repository
Written inC++
Operating systemUnix-like, Microsoft Windows, Mac OS X
PlatformIntel x86 - 32-bit, x64
TypeStatistical package
LicenseNew BSD License
Websitemc-stan.org

Stan is a probabilistic programming language for statistical inference written in C++. The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function.

Stan is licensed under the New BSD License. Stan is named in honour of Stanislaw Ulam, pioneer of the Monte Carlo method.

Stan was created by a development team consisting of 34 members that includes Andrew Gelman, Bob Carpenter, Matt Hoffman, and Daniel Lee.

Interfaces

The Stan language itself can be accessed through several interfaces:

In addition, higher-level interfaces are provided with packages using Stan as backend, primarily in the R language:

  • rstanarm provides a drop-in replacement for frequentist models provided by base R and lme4 using the R formula syntax;
  • brms provides a wide array of linear and nonlinear models using the R formula syntax;
  • prophet provides automated procedures for time series forecasting.

Algorithms

Stan implements gradient-based Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference, stochastic, gradient-based variational Bayesian methods for approximate Bayesian inference, and gradient-based optimization for penalized maximum likelihood estimation.

Automatic differentiation

Stan implements reverse-mode automatic differentiation to calculate gradients of the model, which is required by HMC, NUTS, L-BFGS, BFGS, and variational inference. The automatic differentiation within Stan can be used outside of the probabilistic programming language.

Usage

Stan is used in fields including social science, pharmaceutical statistics, market research, and medical imaging.


See also

  • PyMC is a probabilistic programming language in Python
  • ArviZ a Python library for Exploratory Analysis of Bayesian Models



This page was last updated at 2024-02-19 12:29 UTC. Update now. View original page.

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