---

name: pymc description: "Use PyMC for Bayesian probabilistic programming, model construction, distributions, MCMC/NUTS inference, predictive checks, diagnostics, and advanced GP/ODE/VI workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyMC repo skill

Use this skill when a task needs PyMC-specific operating knowledge: Bayesian model graphs, coordinates and dimensions, pm.Data, distribution/log-probability design, CustomDist, MCMC/NUTS, prior or posterior predictive sampling, DataTree/ArviZ outputs, diagnostics, Gaussian processes, ODE likelihoods, or variational inference.

Start here

• Read references/repo-provenance.md before relying on this skill for a different PyMC checkout or when deciding whether to refresh it.
• Read references/installation-and-environment.md when installing PyMC, checking optional samplers, or choosing nutpie, JAX/NumPyro/BlackJAX, Zarr, mcbackend, Graphviz, or CPU/GPU routes.
• Read references/troubleshooting.md for cross-cutting install/import, PyTensor compiler, optional backend, DataTree/ArviZ, and Graphviz failures.
• Run scripts/check_pymc_env.py to check PyMC importability, versions, optional modules, and an optional tiny model smoke.
• Run scripts/pymc_quick_smoke.py for a tiny end-to-end CPU model, pm.sample, predictions, and pm.do intervention check.

Minimal public install and import check:

python -m pip install pymc
python -c "import pymc as pm; print(pm.__version__)"

For faster optional NUTS support, install pymc[nutpie]. Install numpyro, blackjax, and JAX packages only when a task actually needs JAX-backed samplers.

Route by task

If the user asks about...	Use
Building a model with with pm.Model, named variables, observed data, missing values, coords/dims, pm.Data, pm.set_data, Deterministic, Potential, compile_logp, model debugging, graph visualization, pm.do, or pm.observe	sub-skills/modeling-data/SKILL.md
Choosing distributions, .dist() components, shapes/support dimensions, transforms, CustomDist, DensityDist, mixtures, truncation/censoring, or pm.logp/pm.logcdf	sub-skills/distributions-logprob/SKILL.md
Running pm.sample, NUTS/MCMC, step methods, external samplers (nutpie, numpyro, blackjax), prior/posterior predictive sampling, pm.draw, SMC, diagnostics, log-likelihood, DataTree/ArviZ outputs, Zarr, or mcbackend	sub-skills/inference-predictive/SKILL.md
Gaussian processes, pm.ode.DifferentialEquation, ADVI/FullRankADVI/SVGD/ASVGD, pm.fit, approximation sampling, minibatches, or advanced pymc.dims interactions	sub-skills/advanced-workflows/SKILL.md

Operating defaults

1. Build models in a with pm.Model(coords=...) as model: context. Use pm.Data only for values you intend to replace later.
2. Use regular coords/dims for stable output labels and shape metadata. Treat pymc.dims as experimental and read the modeling/advanced sub-skill before using it.
3. Use .dist() for unregistered component distributions in mixtures, truncation, censoring, and symbolic custom distributions.
4. For deterministic smokes, pin nuts_sampler="pymc", chains=1, cores=1, tiny draws/tune, and compute_convergence_checks=False. Do not treat tiny-draw smokes as statistical convergence evidence.
5. For real inference, increase draws, tune, and chains; inspect divergences, tree depth, ESS, R-hat, and log-likelihood/model-comparison readiness.
6. Prefer default DataTree outputs (return_inferencedata=True). Use pm.compute_log_likelihood when model comparison needs a log_likelihood group.
7. When changing data length for prediction, update both pm.Data values and matching coords, then use posterior predictive guidance from inference-predictive.

Common task chains

• Out-of-sample predictions with changed covariates: read modeling-data for pm.Data/coords mutation, then inference-predictive for sample_posterior_predictive(predictions=True) and output validation.
• Custom likelihood or mixture model: read distributions-logprob for .dist(), support, signature, and logp checks; then inference-predictive for sampling and diagnostics.
• Scientific GP or ODE model: read advanced-workflows for GP/ODE construction; then modeling-data for shared coordinates/data and inference-predictive for posterior/predictive execution.
• Approximate large-data workflow: read advanced-workflows for VI/minibatches, then use inference-predictive only for final posterior predictive and diagnostic output handling.

Self-contained runtime helpers

These helpers are safe, tiny, and CPU-first:

python scripts/check_pymc_env.py --run-smoke --json
python scripts/pymc_quick_smoke.py --draws 10 --tune 10 --json
python sub-skills/modeling-data/scripts/model_data_smoke.py --posterior-predictive --draws 5 --tune 5 --quiet
python sub-skills/distributions-logprob/scripts/distribution_logp_smoke.py --json
python sub-skills/inference-predictive/scripts/inference_smoke.py --draws 5 --tune 5 --json
python sub-skills/advanced-workflows/scripts/advanced_workflows_smoke.py --all --vi-iterations 3 --vi-draws 3 --json

Do not link future work to PyMC source docs, notebooks, tests, or scripts as runtime dependencies. Use this skill's bundled references and scripts, and refresh the skill if a newer checkout changes public APIs or optional backend behavior.