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score_function_evaluator

score_function_evaluator

Evaluate log density, gradient, Hessian, and finite-difference checks for a Bernoulli-Beta Stan score-model proxy in unconstrained coordinates.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: score_function_evaluator description: Evaluate log density, gradient, Hessian, and finite-difference checks for a Bernoulli-Beta Stan score-model proxy in unconstrained coordinates.

Score Function Evaluator

Use this skill to exercise BridgeStan-like score-model access on the Bernoulli recovery proxy. It consumes a Stan contract and transform adapter rather than inventing an unrelated objective.

Inputs

  • Contract JSON from stan_model_contract.
  • Observation data JSON with binary y values.
  • Unconstrained scalar parameter value.
  • Optional path to the transform adapter script.

Outputs

  • JSON with log density, gradient, Hessian, finite-difference diagnostics, and pass/fail checks.

Workflow

  1. Confirm the contract contains a bounded scalar theta and Bernoulli likelihood.
  2. Convert unconstrained z to constrained theta using the transform adapter.
  3. Evaluate the Bernoulli likelihood with Beta(1,1) prior and the transform Jacobian.
  4. Compute analytic gradient and Hessian in z.
  5. Cross-check the derivatives with central finite differences before accepting the result.

Validation

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

Limitations

This is a recovery proxy for the score-model mechanism, not a replacement for full BridgeStan compilation or real Stan autodiff.

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