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name: posterior_predictive_validation description: Validate SNPE-style posterior recovery using parameter correlation, posterior predictive summary error, and mechanism checks.

Posterior Predictive Validation

Use this skill to decide whether a reduced or full simulation-based posterior recovery has exercised the paper mechanism. Do not accept proxy recovery from a metric alone; require mechanism checks.

Inputs

• Posterior mean or posterior samples.
• True parameter if using synthetic data.
• Observed summary and posterior predictive summaries.
• Declared metric threshold and mechanism flags.

Outputs

• Filter correlation metric.
• Posterior predictive summary error.
• Boolean mechanism checks.
• Acceptance decision with reasons.

Workflow

1. Compute centered vector correlation between posterior mean and true parameter.
2. Compute average absolute summary error for posterior predictive summaries.
3. Verify required mechanism flags: simulator executed, summaries conditioned, posterior estimator fit, posterior samples generated, no likelihood evaluation, and no original repository use.
4. Accept only if metric, summary error, and mechanism checks pass.

Validation

Run python tests/test_validation.py from this skill directory.

Limitations

Correlation against synthetic ground truth is a reduced validation target; full paper validation would compare richer posterior distributions and biological traces.