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 http://mc-stan.org/.
- HTML documentation: https://pystan.readthedocs.org
- Issue tracker: https://github.com/stan-dev/pystan/issues
- Source code repository: https://github.com/stan-dev/pystan
- Stan: http://mc-stan.org/
- Stan User's Guide and Reference Manual (pdf) available at http://mc-stan.org
- ArviZ: Exploratory analysis of Bayesian models with Python by @arviz-devs
- Prophet: Timeseries forecasting by @facebook
- Scikit-learn integration: pystan-sklearn by @rgerkin.
- BAMBI: BAyesian Model-Building Interface by @bambinos
- Jupyter tool: StanMagic by @Arvinds-ds
- Jupyter tool: JupyterStan by @janfreyberg
The development of PyStan3 with updated API can be found under stan-dev/pystan-next
Detailed installation instructions can be found in the doc/installation_beginner.md file.
Detailed installation instructions for Windows can be found in docs under PyStan on Windows
NumPy and Cython (version 0.22 or greater) are required. matplotlib is optional. ArviZ is recommended for visualization and analysis.
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 https://github.com/stan-dev/pystan.git cd pystan python setup.py install
To install latest development version user can also use pip
pip install git+https://github.com/stan-dev/pystan
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]}
sm = pystan.StanModel(model_code=schools_code)
fit = sm.sampling(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()
plt.show()
# updated traceplot can be plotted with
import arviz as az
az.plot_trace(fit)