Stan (software)

Probabilistic programming language for Bayesian inference From Wikipedia, the free encyclopedia

Stan (software)

Stan is a probabilistic programming language for statistical inference written in C++.[2] The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function.[2]

Quick Facts Original author(s), Initial release ...
Stan
Original author(s)Stan Development Team
Initial releaseAugust 30, 2012 (2012-08-30)
Stable release
2.36.0[1]  / 10 December 2024; 4 months ago (10 December 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
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Stan is licensed under the New BSD License. Stan is named in honour of Stanislaw Ulam, pioneer of the Monte Carlo method.[2]

Stan was created by a development team consisting of 52 members[3] that includes Andrew Gelman, Bob Carpenter, Daniel Lee, Ben Goodrich, and others.

Example

A simple linear regression model can be described as , where . This can also be expressed as . The latter form can be written in Stan as the following:

data {
  int<lower=0> N;
  vector[N] x;
  vector[N] y;
}
parameters {
  real alpha;
  real beta;
  real<lower=0> sigma;
}
model {
  y ~ normal(alpha + beta * x, sigma);
}

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:[4]

  • rstanarm provides a drop-in replacement for frequentist models provided by base R and lme4 using the R formula syntax;
  • brms[5] 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.[2] The automatic differentiation within Stan can be used outside of the probabilistic programming language.

Usage

Stan is used in fields including social science,[9] pharmaceutical statistics,[10] market research,[11] and medical imaging.[12]

See also

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

References

Further reading

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