pymc-modeling
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
npx skills add majiayu000/claude-skill-registry --skill pymc-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyMC Modeling Bayesian modeling workflow for PyMC v5+ with modern API patterns. **Notebook preference**: Use marimo for interactive modeling unless the project already uses Jupyter. ## Model Specification ### Basic Structure ```python import pymc as pm import arviz as az with pm.Model(coords=coords) as model: # Data containers (for out-of-sample prediction) x = pm.Data("x", x_obs, dims="obs") # Priors beta = pm.Normal("beta", mu=0, sigma=1, dims="features") sigma = pm.HalfNormal("sigma", sigma=1) # Likelihood mu = pm.math.dot(x, beta) y = pm.Normal("y", mu=mu, sigma=sigma, observed=y_obs, dims="obs") # Inference idata = pm.sample() ``` ### Coords and Dims Use coords/dims for interpretable InferenceData when model has meaningful structure: ```python coords = { "obs": np.arange(n_obs), "features": ["intercept", "age", "income"], "group": group_labels, } ``` Skip for simple models where overhead exceeds benefit. ### Parameterization Prefer non-centered parameterization for hierarchical models with weak data: ```python # Non-centered (better for divergences) offset = pm.Normal("offset", 0, 1, dims="group") alpha = mu_alpha + sigma_alpha * offset # Centered (better with strong data) a
- Model Specification
- Basic Structure
- Coords and Dims
- Parameterization
- Inference
- Default Sampling (nutpie)
- PyMC Native Sampling
- Alternative MCMC Backends
- Approximate Inference
- Diagnostics and ArviZ Workflow
- Phase 1: Immediate Checks (Required)
- Phase 2: Deep Convergence (If Phase 1 marginal)
- Phase 3: Model Criticism (Required)
- Phase 4: Parameter Interpretation
What does the pymc-modeling skill do?
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pymc-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.