gituser/production/: pystan-2.19.1.1 metadata and description

Python interface to Stan, a package for Bayesian inference

classifiers	Programming Language :: Python Programming Language :: Python :: 2 Programming Language :: Python :: 3 Programming Language :: Cython Development Status :: 4 - Beta Environment :: Console Operating System :: OS Independent Intended Audience :: Developers Intended Audience :: Science/Research License :: OSI Approved :: GNU General Public License v3 (GPLv3) Topic :: Scientific/Engineering Topic :: Scientific/Engineering :: Information Analysis
description_content_type	text/x-rst
license	GPLv3
maintainer	PyStan Developers
maintainer_email	stan-users@googlegroups.com
requires_dist	Cython (!=0.25.1,>=0.22) numpy (>=1.7)

File	Tox results	History
pystan-2.19.1.1-cp27-cp27mu-linux_x86_64.whl Size 56 MB Type Python Wheel Python 2.7		Uploaded to gituser/production by gituser 2020-03-11 00:19:53
pystan-2.19.1.1-cp37-cp37m-linux_x86_64.whl Size 57 MB Type Python Wheel Python 3.7		Replaced 4 time(s) Uploaded to gituser/production by gituser 2020-03-12 18:01:08

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/.

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

Similar projects

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

PyStan3 / Stan3

The development of PyStan3 with updated API can be found under stan-dev/pystan-next

Detailed Installation Instructions

Detailed installation instructions can be found in the doc/installation_beginner.md file.

Windows Installation Instructions

Detailed installation instructions for Windows can be found in docs under PyStan on Windows

Quick Installation (Linux and macOS)

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.

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()

# updated traceplot can be plotted with
import arviz as az
az.plot_trace(fit)

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