pystan Release 2.18.1.0

Python interface to Stan, a package for Bayesian inference

Keywords
machine-learning, probabilistic-programming, python, stan, statistics
GPL-3.0
Install
``` pip install pystan==2.18.1.0 ```

PyStan: The Python Interface to Stan

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/.

Detailed Installation Instructions

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

Quick Installation (Linux and macOS)

NumPy and Cython (version 0.22 or greater) are required. matplotlib is optional.

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
```

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()
```