PyMC Bayesian Modeler

SkillAI & models

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

Use PyMC Bayesian Modeler in Claude, ChatGPT or Ahel Desktop

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Details

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.

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

PyMC Bayesian ModelerStart free

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: 113
Last commit: Sep 2026

Advanced

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

github.com/a5c-ai/babysitter