pymc-bayesian-modeler
// PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
namepymc-bayesian-modeler
descriptionPyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
allowed-toolsBash,Read,Write,Edit,Glob,Grep
metadata[object Object]
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