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name: sbi_posterior_interface_and_diagnostics description: Summarize posterior samples and validate SBI uncertainty with finite-sample and reference checks.

SBI Posterior Interface And Diagnostics

Use this skill when an SBI workflow has produced posterior samples or a posterior-like object and needs sample summaries and mechanism-focused diagnostics.

Do not use it to train the posterior estimator.

Inputs

• Posterior samples or sample-producing posterior object.
• Observation metadata.
• Optional reference posterior summary.
• Thresholds for mean error and uncertainty sanity checks.

Outputs

• Sample mean, standard deviation, count, and finite status.
• Reference comparison metrics when available.
• Diagnostic pass/fail report and mechanism-check fields.

Workflow

1. Normalize samples to a numeric list or two-dimensional array.
2. Check finite values and non-degenerate uncertainty.
3. Compute summary statistics.
4. Compare with reference values when supplied.
5. Return explicit pass/fail diagnostics without fabricating unavailable log probabilities.

Validation

Run:

python scripts/diagnose_posterior.py --samples-json '[1.0, 1.1, 0.9]'
python tests/test_diagnose_posterior.py

Limitations

• Reference likelihoods are for evaluation only; they must not be required by the SBI training loop.
• Some posterior implementations support sampling but not density evaluation.