pymc-bayesian-modeling
Provides a structured workflow for Bayesian modeling with PyMC, covering data prep, model building, prior/posterior checks, MCMC diagnostics, and variational inference. Includes practical code examples for common tasks like hierarchical models and model comparison.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install k-dense-ai-claude-scientific-skills-pymc
Repository
Skill path: scientific-skills/pymc
Provides a structured workflow for Bayesian modeling with PyMC, covering data prep, model building, prior/posterior checks, MCMC diagnostics, and variational inference. Includes practical code examples for common tasks like hierarchical models and model comparison.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Data / AI, Full Stack.
Target audience: AI/ML teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: K-Dense-AI.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install pymc-bayesian-modeling into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/K-Dense-AI/claude-scientific-skills before adding pymc-bayesian-modeling to shared team environments
- Use pymc-bayesian-modeling for ai/ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.