pymc-bayesian-modeler

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}
graph	{"domains":["domain:physics"],"skillAreas":["skill-area:statistical-analysis","skill-area:mathematical-reasoning","skill-area:data-analysis"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-scientist","role:computational-scientist"]}

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