# Pre-rendered story outputs

These files are deterministic synthetic posterior fixtures used to render the article without requiring a long PyMC-Marketing MCMC run.

The sweep calculation itself is real: `scripts/generate_story_artifacts.py` constructs a well-sampled synthetic reference posterior and passes it through the bundled `pymc-marketing-sweep` engine. The Pareto-k values, reweighting ESS, protected-variable movement, feasibility decisions and recommendation are computed by the actual package.

The bad-fit and final-refit posterior chains are fixtures designed to make the centred-hierarchy failure mode and successful validation stage visually reproducible. They are not measurements from a real company and are not presented as a substitute for a real NUTS fit.

Files include:

- `eda_summary.json`: portfolio-level EDA metrics quoted by the article.
- `bad_fit_diagnostics.csv`: fixture convergence failure.
- `reference_fit_diagnostics.csv`: well-mixed broad-prior reference fixture.
- `fixed_fit_diagnostics.csv`: final validation fixture.
- `selected_price_traces.npz`: selected chain traces used for trace and posterior-overlay figures.
- `price_elasticity_comparison.csv`: truth, bad, reference, sweep and fixed elasticity summaries.
- `sweep_results.csv`: all prior candidates and PSIS diagnostics.
- `sweep/`: recommendation and weighted-posterior artefacts.

Run `./run_demo.sh` in a PyMC-Marketing 1.x environment to create genuine `outputs/runtime/*.nc` fits and runtime diagnostics.
