pymc-modeling

PyMC modeling

Choose a useful starting point: a simple scaffold, an established subject-matter model, or a decomposition of a larger target model. Expand, simplify or branch as the question, data and checks warrant. Use the consuming project's data and Python environment; check installed APIs and package compatibility.

For pymc-extras tasks, start with feature selection and compatibility, then load only the relevant workflow reference. Prefer its supported algorithms to bespoke implementations; do not use an extension merely because it is installed.

Choose the primary task

• Construct, fit or initialize a model: use pymc-modeling.
• Elicit prior assumptions or check prior-predictive plausibility: use prior-elicitation when available.
• Diagnose an existing completed fit or evaluate its predictions: use arviz-diagnostics when available.
• Repair symbolic computation, broadcasting, gradients or compilation: use pytensor-workflows when available.
• Track experiments, preserve MLflow artifacts or restore a logged MMM model: use pymc-mlflow when available.

Multipart requests can use more than one skill. The references below support modeling when specialist skills are not installed.

Workflow

1. Formulate. State the estimand or prediction target, observation units, outcome support, grouping/time structure, selection and missingness, and what information is available at prediction time. Separate predictive, inferential and causal claims; respect data authorization and retain source identities.
2. Specify and inspect. Match the likelihood to the observation process; choose identifiable parameterizations and priors in declared units. Check row alignment, shapes, support and finite initial log density. Ordinary dims label positional tensors; they do not join coordinates. Use known-parameter simulated cases to probe new implementations and their failure modes; use scoped SBC when repeated inference checks are warranted.
3. Check prior predictions. Simulate before fitting and compare support, scales and decision-relevant features to domain knowledge. Do not tune priors solely to the realized extrema.
4. Explore, then fit for inference. Prefer nutpie when compatible and installed. Short preliminary fits can reveal bugs, difficult geometry or gross misfit; label them exploratory, not reliable posterior inference. For reportable results, allocate warmup and draws from adaptation and required ESS/MCSE. Save results before diagnostics or plotting.
5. Diagnose, then criticize. Inspect divergences, rank R-hat, bulk/tail ESS, MCSE and chain/rank plots. Diagnose every divergence. Unreliable computation leaves posterior conclusions unresolved; it does not disprove a likelihood. Use posterior predictive discrepancies to check the observation model. In-sample PPCs are not held-out validation.
6. Predict, decide and revise. Preserve training identities and align new inputs; distinguish latent means from noisy observations. For recommendations, propagate uncertainty through stated consequences, utilities and constraints, or hand off the inference without inventing the decision maker's values. Retain relevant model variants and explain how changed assumptions affect estimands, predictions and decisions—not just which has the best score. Adaptive PPC/CV-driven search can overfit; do not present its winner as uniquely confirmed or change seeds/criteria merely to obtain a favorable result.

PyMC 6 returns xarray.DataTree: use idata["posterior"], not attribute group access. Call pm.compute_log_likelihood(idata, model=model) explicitly before LOO or other pointwise-likelihood workflows. ArviZ 1.0 uses LOO, not removed WAIC; state interval probability and type (the default is 89% ETI). Similar stacking weights do not imply model equivalence. Sampler preferences, warmup starting points and interval defaults are implementation conventions, not universal Bayesian workflow requirements. Choose them for the task.

Read only what the task needs

When working on	Reference
End-to-end fitting, diagnostics and scientific checks	Workflow
Model construction, data containers and dimensions	Model and data
Failed initialization, shape errors, stuck sampling or warnings	Troubleshooting
Group effects, pooling and parameterization	Hierarchical models
Outcome support, censoring, multivariate families and Jacobians	Likelihoods
Algorithms, backends, budgets and persistence	Sampling
pymc-extras feature selection, compatibility and validation	PyMC Extras
Markov chains, specialized counts, extreme values and histogram likelihoods	Extras distributions
Kalman-marginalized models, structural components and forecasting	Extras state-space workflows
Prior factories, model packaging, VIP and posterior-as-prior transfer	Extras model tools
Variational, Laplace and Pathfinder approximations	Approximate inference
Covariance models and finite-basis error	Gaussian processes
Temporal dependence, differential equations and forecasting	Time series
Label invariance and discrete elimination	Mixtures
External densities, simulators and predictive callbacks	Custom likelihoods
Conditioning, interventions and graph transformations	Transformations
In-sample criticism and new-input prediction	Predictive checks
Tree ensembles and prediction state	BART
Basis penalties and distributional predictors	Splines
Simulated-data experiments, SBC, numerical oracles and mocks	Model testing
Advanced numerical and extension interfaces	Interface boundaries

For version-specific APIs, use help() or inspect.signature() on the installed object in the consuming environment, then consult version-matched official documentation or source.