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name: advanced-workflows description: "Apply PyMC Gaussian processes, ODE likelihood models, variational inference, minibatches, and experimental dimensional APIs at a practical workflow level." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyMC advanced workflows

Use this sub-skill when a PyMC task involves Gaussian processes, ODE likelihoods, variational inference/OPVI, minibatches, or experimental dimension-aware APIs in advanced models.

Read/run map

• Read references/gaussian-processes.md when choosing pm.gp means/covariances, Latent, Marginal, sparse/marginal approximations, HSGP, or GP conditionals.
• Read references/ode-workflows.md when building pm.ode.DifferentialEquation likelihood models or debugging func, times, y0, theta, n_states, and n_theta shapes.
• Read references/variational-inference.md when using pm.fit, ADVI, FullRankADVI, SVGD/ASVGD, callbacks, sample_approx, or pm.Minibatch.
• Read references/troubleshooting.md for GP shapes, ODE return types, VI convergence, minibatch scaling, or approximation failures.
• Run scripts/advanced_workflows_smoke.py to check tiny CPU-only GP, ODE, and VI workflows.

Route ordinary model/data syntax to ../modeling-data/SKILL.md, distribution/logp internals to ../distributions-logprob/SKILL.md, and standard MCMC/predictive outputs to ../inference-predictive/SKILL.md.

Operating procedure

1. Identify the advanced family first: GP, ODE, VI/minibatch, or experimental dims interaction.
2. Build only the family-specific model component here; pull base model/data and likelihood details from siblings when needed.
3. Choose inference deliberately. Prefer ordinary pm.sample for final posterior quality when feasible; use VI for fast approximate fitting, initialization, large data/minibatches, or exploratory checks.
4. Add the smallest possible smoke check: GP covariance or conditional shape, ODE forward simulation shape, or low-iteration VI fit plus approximation sample.