What is the PyMC skill for Claude Code?

It is a specialized capability that guides Claude in building, sampling, and diagnosing Bayesian models using the PyMC library for probabilistic programming.

How does it assist with model comparison?

The skill provides scripts to compare multiple models using information criteria like LOO and WAIC, helping you select the most appropriate model for your data.

Can this skill handle hierarchical models?

Yes, it includes specific patterns and best practices like non-centered parameterization to ensure efficient sampling of hierarchical and multilevel structures.

Does it help with MCMC diagnostics?

Absolutely. The skill provides workflows for checking R-hat, Effective Sample Size (ESS), and divergences to ensure your model has converged correctly.

What version of PyMC does this skill support?

It is designed for modern PyMC (version 5.x+) and integrates seamlessly with ArviZ for comprehensive posterior analysis and visualization.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: youyinnn

byyouyinnn

0•

Data Science & ML

Builds, fits, and validates sophisticated Bayesian statistical models using PyMC's modern probabilistic programming interface.

The PyMC skill empowers users to implement robust Bayesian inference workflows directly within Claude. It provides a structured approach to building hierarchical models, performing MCMC sampling with NUTS, and conducting critical diagnostic checks like R-hat and ESS. Whether you are performing uncertainty quantification, handling missing data, or comparing complex models using LOO/WAIC criteria, this skill ensures best practices—such as non-centered parameterization and prior predictive validation—are integrated into your data science projects.

Key Features

01Advanced diagnostic reporting including R-hat, ESS, and divergence checks

02Templates for hierarchical, logistic, and time-series Bayesian models

03Model comparison and averaging using LOO and WAIC information criteria

04Implementation of MCMC (NUTS) and Variational Inference (ADVI) workflows

05Automatic setup for prior and posterior predictive checks with ArviZ

060 GitHub stars

Use Cases

01Performing robust regression and time-series forecasting with Bayesian methods

02Implementing principled handling of missing data and measurement errors

03Quantifying uncertainty in complex hierarchical or multilevel data structures

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection pymc

For use in Claude.ai and ChatGPT