# pystan

**Repository Path**: mirrors/pystan

## Basic Information

- **Project Name**: pystan
- **Description**: PyStan 为 Stan 提供了一个 Python 接口，这是一个使用 No-U-Turn 采样器进行贝叶斯推理的软件包，这是Hamiltonian Monte Carlo 的一
- **Primary Language**: Python
- **License**: ISC
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/pystan
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2019-01-04
- **Last Updated**: 2026-01-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

******
PyStan
******

**PyStan** is a Python interface to Stan, a package for Bayesian inference.

Stan® is a state-of-the-art platform for statistical modeling and
high-performance statistical computation. Thousands of users rely on Stan for
statistical modeling, data analysis, and prediction in the social, biological,
and physical sciences, engineering, and business.

Notable features of PyStan include:

* Automatic caching of compiled Stan models
* Automatic caching of samples from Stan models
* An interface similar to that of RStan
* Open source software: ISC License

Getting started
===============

Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0.

The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model.

.. code-block:: python

    import stan

    schools_code = """
    data {
      int<lower=0> J;         // number of schools
      array[J] real y;              // estimated treatment effects
      array[J] real<lower=0> sigma; // standard error of effect estimates
    }
    parameters {
      real mu;                // population treatment effect
      real<lower=0> tau;      // standard deviation in treatment effects
      vector[J] eta;          // unscaled deviation from mu by school
    }
    transformed parameters {
      vector[J] theta = mu + tau * eta;        // school treatment effects
    }
    model {
      target += normal_lpdf(eta | 0, 1);       // prior log-density
      target += normal_lpdf(y | theta, sigma); // log-likelihood
    }
    """

    schools_data = {"J": 8,
                    "y": [28,  8, -3,  7, -1,  1, 18, 12],
                    "sigma": [15, 10, 16, 11,  9, 11, 10, 18]}

    posterior = stan.build(schools_code, data=schools_data)
    fit = posterior.sample(num_chains=4, num_samples=1000)
    eta = fit["eta"]  # array with shape (8, 4000)
    df = fit.to_frame()  # pandas `DataFrame`


Citation
========

We appreciate citations as they let us discover what people have been doing
with the software. Citations also provide evidence of use which can help in
obtaining grant funding.

To cite PyStan in publications use:

Riddell, A., Hartikainen, A., & Carter, M. (2021). PyStan (3.0.0). https://pypi.org/project/pystan

Or use the following BibTeX entry::

    @misc{pystan,
      title = {pystan (3.0.0)},
      author = {Riddell, Allen and Hartikainen, Ari and Carter, Matthew},
      year = {2021},
      month = mar,
      howpublished = {PyPI}
    }

Please also cite Stan.