pymc-bayesian-modeling - Build and validate Bayesian models with PyMC
Builds, fits, validates, diagnoses, and compares Bayesian models with PyMC, including hierarchical models, MCMC, variational inference, predictive checks, and LOO/WAIC.
Tags
Updated: 2026-09-30Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build Bayesian models
- Fit models with MCMC
- Run variational inference
- Check prior predictions
- Check posterior predictions
- Diagnose sampling issues
- Compare models with LOO/WAIC
- Generate posterior predictions
- Analyze posterior results
Inputs
- Datasets
- Predictor and outcome values
- Grouping or time-series structure
- New predictor values
- Model specifications
- Fitted model outputs
Outputs
- Fitted Bayesian model results
- Posterior samples
- Sampling diagnostics
- Predictive check results
- Model comparison results
- Prediction intervals
- Model-averaged predictions
Requirements
- Python environment
- PyMC 5.x or later
- ArviZ package
- NumPy package