PyStan: The Python Interface to Stan

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.. tip:: PyStan 3 is available for Linux and macOS users. Visit the PyStan 3 documentation <https://pystan.readthedocs.io/en/latest/>_ for details. PyStan 2 is not maintained.

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 <http://mc-stan.org>_ and its modeling language, see the Stan User's Guide and Reference Manual at http://mc-stan.org/ <http://mc-stan.org/>_.

Important links

• HTML documentation: https://pystan2.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

Related projects

• ArviZ: Exploratory analysis of Bayesian models with Python <https://github.com/arviz-devs/arviz>_ by @arviz-devs
• Jupyter tool: StanMagic <https://github.com/Arvinds-ds/stanmagic>_ by @Arvinds-ds
• Jupyter tool: JupyterStan <https://github.com/janfreyberg/jupyterstan>_ by @janfreyberg
• Scikit-learn integration: pystan-sklearn <https://github.com/rgerkin/pystan-sklearn>_ by @rgerkin.

Projects using PyStan

• BAMBI: BAyesian Model-Building Interface <https://github.com/bambinos/bambi>_ by @bambinos
• hBayesDM: hierarchical Bayesian modeling of Decision-Making tasks <https://hbayesdm.readthedocs.io>_ by @CCS-Lab
• Orbit: Object-oRiented BayesIan Timeseries models <https://github.com/uber/orbit>_ by @uber
• Prophet: Timeseries forecasting <https://facebook.github.io/prophet/>_ by @facebook

Similar projects

• PyMC3: https://docs.pymc.io/
• emcee: https://emcee.readthedocs.io/en/stable/

PyStan3 / Stan3

The development of PyStan3 with updated API can be found under stan-dev/pystan-next <https://github.com/stan-dev/pystan-next>_

Detailed Installation Instructions

Detailed installation instructions can be found in the doc/installation_beginner.md <doc/installation_beginner.rst/>_ file.

Windows Installation Instructions

Detailed installation instructions for Windows can be found in docs under PyStan on Windows <https://pystan2.readthedocs.io/en/latest/windows.html>_

Quick Installation (Linux and macOS)

NumPy <http://www.numpy.org/>_ and Cython <http://www.cython.org/>_ (version 0.22 or greater) are required. matplotlib <http://matplotlib.org/>_ is optional. ArviZ is recommended for visualization and analysis.

PyStan and the required packages may be installed from the Python Package Index <https://pypi.python.org/pypi>_ 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.

make (mingw32-make on Windows) is a requirement for building from source.

Example

.. code-block:: python

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)

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