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name: proposal_posterior_transform description: Apply APT proposal-prior log-density corrections to candidate posterior scores for likelihood-free inference.

Proposal Posterior Transform

Use this skill when implementing Automatic Posterior Transformation (APT) training on simulations drawn from a proposal distribution rather than the prior. Do not use it for ordinary prior-only neural posterior estimation, where proposal and prior corrections cancel.

Inputs

• model_log_scores: posterior log-score values for the same candidate atoms.
• prior_log_probs: log prior density for every candidate atom.
• proposal_log_probs: log proposal density for every candidate atom.

Outputs

• corrected_logits: model_log_score + proposal_log_prob - prior_log_prob for every atom.
• probabilities: numerically stable softmax probabilities over corrected logits.

Workflow

1. Confirm all arrays have equal non-zero length.
2. Add the proposal-minus-prior correction to every model score.
3. Normalize with a max-subtracted softmax.
4. Propagate impossible support through -inf log densities and fail if the normalized mass is invalid.

Validation

Run python scripts/apt_transform.py --self-test or the skill-tree validator with tests enabled.

Limitations

This skill implements deterministic correction math only. It does not train a neural density estimator or choose proposal schedules.