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.
PyStan aims to reproduce the functionality present in RStan. There are a few features present in RStan that have yet to be implemented in PyStan. If you find a feature missing that you use frequently please file a bug report so developers can better direct their efforts.
- Source code repo: https://github.com/stan-dev/pystan
- HTML documentation: https://pystan.readthedocs.org
- Issue tracker: https://github.com/stan-dev/pystan/issues
- Stan: http://mc-stan.org/
NumPy and Cython (version 0.19.1 or greater) are required. matplotlib is optional.
PyStan and the required packages may be installed from the Python Package Index using pip.
pip install numpy Cython pip install pystan
Alternatively, if Cython (version 0.19 or greater) and NumPy are already available, PyStan may be installed from source with the following commands
git clone https://github.com/stan-dev/pystan.git cd pystan python setup.py install
If you encounter an ImportError after compiling from source, try changing
out of the source directory before attempting import pystan. For example, on
Linux and OS X cd /tmp would work.
import 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)
print(fit)
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
fit.plot()