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name: sbi_neural_inference_family_selector description: Select SNPE, SNLE, or SNRE for a simulation-based inference task from estimator and posterior-access requirements.

SBI Neural Inference Family Selector

Use this skill when deciding which neural SBI family best matches a simulator-based inference problem.

Do not use it to run training; it only emits a family recommendation and constraints.

Inputs

• Desired estimator target: posterior, likelihood, or ratio.
• Whether direct amortized posterior sampling is needed.
• Whether MCMC posterior sampling is acceptable.
• Whether density evaluation is required.

Outputs

• Recommended family: SNPE, SNLE, or SNRE.
• Objective description.
• Posterior construction requirements and limitations.

Workflow

1. Prefer SNPE when the task needs direct conditional posterior estimation and amortized posterior sampling.
2. Prefer SNLE when the task needs learned likelihoods and can use MCMC for posterior sampling.
3. Prefer SNRE when density-ratio classification is appropriate and MCMC is acceptable.
4. Reject incompatible requests instead of overstating a family capability.

Validation

Run:

python scripts/select_family.py --target posterior --direct-sampling
python tests/test_select_family.py

Limitations

• SNRE estimates ratios, not direct likelihood values.
• Posterior density evaluation depends on the chosen posterior implementation.