Can I compare different models using this skill?

Absolutely. It includes workflows for model comparison using LOO (Leave-One-Out) and WAIC, along with guidance on interpreting the weights and reliability of these metrics.

Which version of PyMC is this designed for?

This skill is optimized for modern PyMC version 5.x and above, utilizing the ArviZ library for posterior analysis.

How does it help with sampling issues like divergences?

It provides a diagnostic checklist and specific reparameterization techniques (like increasing target_accept or using non-centered priors) to resolve NUTS sampling errors.

What is the PyMC Claude Code Skill?

It is a specialized capability that enables Claude to provide domain-specific guidance, code templates, and best practices for Bayesian modeling using the PyMC library.

Does this skill support hierarchical models?

Yes, it specifically includes patterns for non-centered parameterization to help avoid divergences in hierarchical and multi-level data structures.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: K-Dense-AI

byK-Dense-AI

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3,718

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데이터 과학 및 ML

Facilitates advanced Bayesian statistical modeling and probabilistic programming using the PyMC and ArviZ ecosystems.

The PyMC Bayesian Modeling skill provides a comprehensive framework for building, fitting, and validating probabilistic models within Claude. It streamlines the complex Bayesian workflow—from data preparation and prior predictive checks to MCMC sampling and posterior diagnostics—ensuring that users follow best practices for hierarchical modeling and uncertainty quantification. By incorporating specialized diagnostic scripts and model comparison techniques like LOO and WAIC, this skill helps researchers and data scientists minimize sampling errors, resolve divergences, and generate reliable statistical inferences using modern PyMC 5.x standards.

주요 기능

01Standardized MCMC sampling workflow using NUTS for high-dimensional models.

02Integrated Variational Inference (ADVI) for fast model exploration and initialization.

03Pre-configured templates for hierarchical, linear, logistic, and time-series models.

043,718 GitHub stars

05Advanced model comparison using LOO/WAIC and Pareto-k reliability checks.

06Automated diagnostics for R-hat, Effective Sample Size (ESS), and divergences.

사용 사례

01Performing principled uncertainty quantification for predictive analytics and scientific research.

02Developing hierarchical models to account for multi-level data structures and group effects.

03Implementing robust regression models with custom priors for small or noisy datasets.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k-dense-ai/claude-scientific-skills pymc

For use in Claude.ai and ChatGPT

Download Skill