stan-dev / Pystan2
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Projects that are alternatives of or similar to Pystan2
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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