name: pymc-bayesian-modeler description: PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis allowed-tools:
- Bash
- Read
- Write
- Edit
- Glob
- Grep metadata: specialization: physics domain: science category: data-analysis phase: 6
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
- Model Building: Construct probabilistic models
- Priors: Specify informative or weakly informative priors
- Sampling: Use NUTS for efficient sampling
- Diagnostics: Check convergence with trace plots and r-hat
- Comparison: Compare models with information criteria
Tools/Libraries
- PyMC
- arviz
- Theano/JAX