PyStan

Release v3.0.0b7

Caution

This documentation describes a BETA release of PyStan 3.

PyStan is a Python interface to Stan, a package for Bayesian inference.

Stan® is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.

Notable features of PyStan include:

  • Automatic caching of compiled Stan models
  • Automatic caching of samples from Stan models
  • An interface similar to that of RStan
  • Open source software: ISC License

(Upgrading from PyStan 2? We have you covered: Upgrading to Newer Releases.)

Quick start

Tip

Pre-release versions must be installed with python3 -m pip install --pre pystan.

Install PyStan with python3 -m pip install pystan. (PyStan requires Python 3.7 or higher running on a Linux or macOS system.)

This block of code shows how to use PyStan with a hierarchical model used to study coaching effects across eight schools (see Section 5.5 of Gelman et al. (2003)).

import stan

schools_code = """
data {
  int<lower=0> J;         // number of schools
  real y[J];              // estimated treatment effects
  real<lower=0> sigma[J]; // standard error of effect estimates
}
parameters {
  real mu;                // population treatment effect
  real<lower=0> tau;      // standard deviation in treatment effects
  vector[J] eta;          // unscaled deviation from mu by school
}
transformed parameters {
  vector[J] theta = mu + tau * eta;        // school treatment effects
}
model {
  target += normal_lpdf(eta | 0, 1);       // prior log-density
  target += normal_lpdf(y | theta, sigma); // log-likelihood
}
"""

schools_data = {"J": 8,
                "y": [28,  8, -3,  7, -1,  1, 18, 12],
                "sigma": [15, 10, 16, 11,  9, 11, 10, 18]}

posterior = stan.build(schools_code, data=schools_data)
fit = posterior.sample(num_chains=4, num_samples=1000)
eta = fit["eta"]  # array with shape (8, 4000)
df = fit.to_frame()  # pandas `DataFrame, requires pandas