pymc-bayesian-modeler

# PyMC Bayesian Modeler ## Purpose Provides expert guidance on PyMC for Bayesian modeling in physics, including hierarchical models and advanced inference methods. ## Capabilities - Probabilistic model construction - NUTS/HMC sampling - Variational inference - Gaussian processes - Model comparison (WAIC, LOO) - Prior predictive checks ## Usage Guidelines 1. **Model Building**: Construct probabilistic models 2. **Priors**: Specify informative or weakly informative priors 3. **Sampling**: Use NUTS for efficient sampling 4. **Diagnostics**: Check convergence with trace plots and r-hat 5. **Comparison**: Compare models with information criteria ## Tools/Libraries - PyMC - arviz - Theano/JAX