pymc-bayesian-modeling - Build and validate Bayesian models with PyMC
Build, fit, validate, diagnose, compare, and use Bayesian models with PyMC, including hierarchical models, MCMC, variational inference, LOO, and WAIC.
Tags
Updated: 2026-10-02Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build Bayesian models
- Fit models with MCMC
- Run variational inference
- Check prior predictions
- Diagnose sampling issues
- Check posterior predictions
- Compare models with LOO
- Compare models with WAIC
- Make posterior predictions
Inputs
- Predictor data
- Outcome data
- Model specifications
- New predictor values
- Fitted inference data
Outputs
- Fitted Bayesian models
- Inference data
- Diagnostic results
- Posterior summaries
- Predictive distributions
- Model comparison results
- Model averaging weights
Requirements
- Python environment
- PyMC 5.x or later
- ArviZ
- NumPy