pymc-bayesian-modeling - Bayesian modeling and inference with PyMC
Build, fit, validate, compare, and use Bayesian models with PyMC, including hierarchical models, MCMC, variational inference, and predictive checks.
Tags
Updated: 2026-10-02Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build Bayesian models
- Run MCMC sampling
- Apply variational inference
- Check prior predictions
- Check posterior predictions
- Diagnose sampling convergence
- Compare models with LOO/WAIC
- Make posterior predictions
- Handle missing data
Inputs
- Observed data
- Predictor variables
- Outcome variables
- Model specifications
- New predictor values
- Model comparison criteria
Outputs
- Fitted Bayesian models
- Posterior samples
- Sampling diagnostics
- Predictive checks
- Model comparison results
- Prediction intervals
Requirements
- Python environment
- PyMC 5.x or later
- ArviZ
- NumPy