PyMC Bayesian Modeler

SkillAI & models

PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the PyMC Bayesian Modeler skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/physics/skills/pymc-bayesian-modeler/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: pymc-bayesian-modeler
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter