PyStan — pystan 3.10.0 documentation (original) (raw)

Release v3.10.0

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:

(Upgrading from PyStan 2? We have you covered: Upgrading to Newer Releases.)

Quick start

Install PyStan with python3 -m 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.

This block of code shows how to use PyStan with a hierarchical model used to study coaching effects across eight schools (see Section 5.5 of Gelman et al. (2003)).

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, requires pandas

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