pymc-bayesian-modeling - Build and validate Bayesian models with PyMC
Build, fit, validate, diagnose, and compare Bayesian models with PyMC using MCMC, variational inference, and predictive checks.
Tags
Updated: 2026-09-30Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build Bayesian models
- Run NUTS sampling
- Perform variational inference
- Check prior predictions
- Diagnose sampling convergence
- Check posterior predictions
- Compare models with LOO
- Compare models with WAIC
- Generate posterior predictions
- Average model predictions
Inputs
- Observed data
- Predictor values
- Model specifications
- New predictor values
- Model inference data
Outputs
- Fitted model results
- Sampling diagnostics
- Posterior summaries
- Predictive distributions
- Model comparison results
- Prediction intervals
Requirements
- Python environment
- PyMC 5.x or later
- ArviZ
- NumPy