PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
Scanned 9/2/2026
Install to Claude Code
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---
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
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