PyMC for flexible Bayesian modeling
Scanned 9/2/2026
Install to Claude Code
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---
name: pymc-probabilistic-programming
description: PyMC for flexible Bayesian modeling
allowed-tools:
- Bash
- Read
- Write
- Edit
- Glob
- Grep
metadata:
specialization: mathematics
domain: science
category: statistical-computing
phase: 6
graph:
domains: [domain:mathematics]
specializations: [specialization:computational-mathematics]
skillAreas: [skill-area:statistical-analysis, skill-area:mathematical-reasoning, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-scientist, role:data-scientist]
---
# PyMC Probabilistic Programming
## Purpose
Provides PyMC capabilities for flexible Bayesian modeling and probabilistic programming in Python.
## Capabilities
- Hierarchical model specification
- Custom distributions
- Gaussian processes
- MCMC and variational inference
- Model diagnostics
- ArviZ integration for visualization
## Usage Guidelines
1. **Model Building**: Use PyMC context managers
2. **Custom Distributions**: Define distributions when needed
3. **Hierarchical Models**: Build proper hierarchical structures
4. **Visualization**: Use ArviZ for diagnostic plots
## Tools/Libraries
- PyMC
- ArviZ
- Theano/PyTensor
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