pystan 2.17.0.0

Python interface to Stan, a package for Bayesian inference

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 <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://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

Related projects
----------------

- Scikit-learn integration: `pystan-sklearn <https://github.com/rgerkin/pystan-sklearn>`_ by @rgerkin.

Similar projects
----------------

- PyMC: http://pymc-devs.github.io/pymc/

Detailed Installation Instructions
----------------------------------
Detailed installation instructions can be found in the
`doc/installation_beginner.md <https://github.com/chendaniely/pystan/blob/develop/doc/installation_beginner.rst/>`_ file.

Quick Installation
------------------

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

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

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.

Example
-------

::

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()

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