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Latest commit 77d183c Feb 18, 2017 @ariddell ariddell committed on GitHub Merge pull request #300 from LMescheder/develop
Expose constrain_pars method


PyStan: The Python Interface to Stan

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PyStan provides a Python interface to Stan, a package for Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo.

For more information on Stan and its modeling language, see the Stan User's Guide and Reference Manual at

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Detailed Installation Instructions

Detailed installation instructions can be found in the doc/ file.

Quick Installation

NumPy and Cython (version 0.22 or greater) are required. matplotlib is optional.

PyStan and the required packages may be installed from the Python Package Index using pip.

pip install pystan

Alternatively, if Cython (version 0.22 or greater) and NumPy are already available, PyStan may be installed from source with the following commands

git clone --recursive
cd pystan
python install

If you encounter an ImportError after compiling from source, try changing out of the source directory before attempting import pystan. On Linux and OS X cd /tmp will work.


import pystan
import numpy as np
import matplotlib.pyplot as plt

schools_code = """
data {
    int<lower=0> J; // number of schools
    real y[J]; // estimated treatment effects
    real<lower=0> sigma[J]; // s.e. of effect estimates
parameters {
    real mu;
    real<lower=0> tau;
    real eta[J];
transformed parameters {
    real theta[J];
    for (j in 1:J)
        theta[j] = mu + tau * eta[j];
model {
    eta ~ normal(0, 1);
    y ~ normal(theta, sigma);

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

fit = pystan.stan(model_code=schools_code, data=schools_dat,
                  iter=1000, chains=4)


eta = fit.extract(permuted=True)['eta']
np.mean(eta, axis=0)

# if matplotlib is installed (optional, not required), a visual summary and
# traceplot are available