Applied Bayesian Computing in Python

ROS examples, BDA demos, CmdStanPy, BlackJAX, and Oryx

Overview

This book is now a working scaffold for Applied Bayesian Computing in Python.

It combines two Aki Vehtari/Gelman-adjacent code bases:

• ROS-Examples: applied regression, prediction, causal inference, model checking, and multilevel examples for Regression and Other Stories.
• BDA_py_demos: Python demos for Bayesian Data Analysis, 3rd ed., including posterior simulation, grid approximations, importance sampling, diagnostics, and CmdStanPy examples.

The organizing principle is practical: reproduce the examples in Python, then show which computational layer is best for each job.

• pandas / numpy / scipy / statsmodels / scikit-learn for ordinary data work and classical regression baselines.
• CmdStanPy for readable Bayesian models and Stan-backed inference.
• BlackJAX for explicit JAX log-density + sampler workflows.
• Oryx where generative-program transformations clarify the model.

Current status: full ROS inventory, BDA notebook inventory, a rendered book scaffold, and several substantive Python translations. The remaining generated pages are placeholders to make coverage auditable as the port proceeds.