BDA Python demos spine

This section incorporates Aki Vehtari’s BDA_py_demos, a Python demo collection for Bayesian Data Analysis, 3rd ed.

The widened project scope is now Applied Bayesian Computing in Python:

ROS examples provide applied regression, causal inference, prediction, model checking, and multilevel-modeling examples.
BDA demos provide foundational Bayesian computation: grid calculations, conjugate models, posterior simulation, importance sampling, diagnostics, and CmdStanPy workflows.
CmdStanPy is the model-first Bayesian engine.
BlackJAX/Oryx are used selectively for explicit log-density/sampler mechanics and JAX-native workflows.

The notebooks below are included as source material first; as the book matures, each will get an accompanying translation/commentary page that connects it to the ROS examples and the CmdStanPy/BlackJAX/Oryx patterns.

# BDA Python demos spine

This section incorporates Aki Vehtari's [`BDA_py_demos`](https://github.com/avehtari/BDA_py_demos), a Python demo collection for *Bayesian Data Analysis*, 3rd ed.

The widened project scope is now **Applied Bayesian Computing in Python**:

- **ROS examples** provide applied regression, causal inference, prediction, model checking, and multilevel-modeling examples.
- **BDA demos** provide foundational Bayesian computation: grid calculations, conjugate models, posterior simulation, importance sampling, diagnostics, and CmdStanPy workflows.
- **CmdStanPy** is the model-first Bayesian engine.
- **BlackJAX/Oryx** are used selectively for explicit log-density/sampler mechanics and JAX-native workflows.

The notebooks below are included as source material first; as the book matures, each will get an accompanying translation/commentary page that connects it to the ROS examples and the CmdStanPy/BlackJAX/Oryx patterns.