PyStan has an interface similar to that of RStan. For an introduction to Stan and RStan see http://mc-stan.org/ and RStan Getting Started.
- Source code repo: https://github.com/ariddell/pystan
- HTML documentation: http://pystan.readthedocs.org
- Issue tracker: https://github.com/ariddell/pystan/issues
- Stan: http://mc-stan.org/
NumPy and Cython are required.
git clone https://github.com/ariddell/pystan.git
cd pystan
python setup.py installimport pystan
import numpy as np
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)