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생산성 및 협업 · SKILL.md

inference-predictive

inference-predictive

Run, configure, validate, and troubleshoot PyMC inference and predictive workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: inference-predictive description: "Run, configure, validate, and troubleshoot PyMC inference and predictive workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyMC inference and predictive workflows

Use this sub-skill when the task is to run or debug PyMC posterior inference, NUTS/MCMC configuration, explicit step methods, external NUTS backends, prior or posterior predictive sampling, pm.draw, SMC, diagnostics, or trace/output backends.

Runtime map

Default stance

  1. Prefer default return_inferencedata=True and work with DataTree groups such as posterior, sample_stats, posterior_predictive, predictions, and log_likelihood.
  2. For reproducible local smokes, callbacks, custom traces, or mixed discrete/continuous models, pin nuts_sampler="pymc".
  3. With nuts_sampler=None, current PyMC may auto-select nutpie if installed and compatible. Pin the sampler explicitly when backend choice matters.
  4. External NUTS (nutpie, numpyro, blackjax) is for all-continuous differentiable models and does not support custom trace, per-draw callback, or return_inferencedata=False.
  5. Use pm.sample_prior_predictive(draws=...), not older samples=....
  6. Validate real inference with divergences, tree depth, ESS, R-hat, sample size, and log-likelihood/model-comparison readiness.

Route model/data design to ../modeling-data/SKILL.md, custom distributions/logp to ../distributions-logprob/SKILL.md, and VI/ADVI/SVGD to ../advanced-workflows/SKILL.md.

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