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name: sir_posterior_refinement description: Apply sampling importance resampling to refine variational posterior samples using likelihood-prior weights.

sir_posterior_refinement

Use this skill when a recovery or implementation needs the paper mechanism from Variational Methods for Simulation-Based Inference without reading the original repository. Do not use it as a full replacement for large-scale sbi or sbibm benchmark training when those runtimes are available.

Inputs

• Numeric simulator, posterior, likelihood, or validity data matching the module contract.
• A bounded runtime where deterministic scripts can be executed.
• Optional random seed for reproducible proxy checks.

Outputs

• JSON-serializable diagnostics from the module script.
• Numeric values that can be cross-checked by a recovery harness.
• Clear flags when the behavior is reduced or proxy-only.

Workflow

1. Read the caller-provided data and keep source boundaries explicit.
2. Run the deterministic script in scripts/ for the relevant SNVI component.
3. Check numeric invariants such as normalized weights, data accumulation, posterior movement, or validity correction.
4. Return diagnostics and preserve logs for downstream recovery validation.

Validation

Run python scripts/ only through the documented tests, or run python -m pytest tests when pytest is available. The Distiller validator command is python <distiller>/module-to-skill/scripts/validate_skill_tree.py <skill_dir> --run-tests.

Limitations

This skill captures reusable SNVI mechanisms. It does not claim to reproduce the paper's full C2ST benchmark curves or pyloric-network experiment unless paired with the required packages, simulators, and compute budget.