PyMC Modeling

Modern Bayesian modeling with PyMC 6+ on the ArviZ 1.1 / PyTensor 3 stack. Key defaults: nutpie sampler (2-5x faster; PyMC 6 selects it automatically when installed — no nuts_sampler argument needed), non-centered parameterization for hierarchical models, HSGP over exact GPs, coords/dims for readable DataTree output, and save-early workflow to prevent data loss from late crashes.

pm.sample(...) returns an xarray.DataTree — the idata name is kept by convention, but it is a DataTree, not the old InferenceData. Access groups by bracket: idata["posterior"], idata["sample_stats"], etc.

Modeling strategy: Build models iteratively — start simple, check prior predictions, fit and diagnose, check posterior predictions, expand one piece at a time. See references/workflow.md for the full workflow.

Model Specification

Basic Structure

import pymc as pm
import arviz as az