Which inference methods are supported?

The skill supports modern PyMC 5.x+ methods, including NUTS (No-U-Turn Sampler) for MCMC, and Automatic Differentiation Variational Inference (ADVI) for faster approximations.

What is the PyMC Bayesian Modeling skill?

This skill provides specialized guidance for Claude to help users build, sample, and diagnose Bayesian models using the PyMC Python library and the ArviZ diagnostic suite.

How does it handle model validation?

It guides users through the complete Bayesian workflow, including prior predictive checks, posterior predictive checks (PPC), and strict diagnostic evaluations like R-hat and Effective Sample Size (ESS).

Does this skill support hierarchical models?

Yes, it includes specific implementation patterns for multilevel modeling, emphasizing critical techniques like non-centered parameterization to ensure sampling convergence.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: pathvanasoftware

bypathvanasoftware

0•

Data Science & ML

Builds and validates sophisticated Bayesian probabilistic models using the PyMC library for advanced statistical inference.

The PyMC Bayesian Modeling skill empowers developers and data scientists to implement robust probabilistic programming workflows directly within Claude Code. It provides domain-specific guidance for building hierarchical models, performing MCMC sampling with the NUTS sampler, and executing variational inference (ADVI). By integrating best practices for prior/posterior predictive checks and model diagnostics via ArviZ, this skill ensures statistical rigor in uncertainty quantification, missing data handling, and model comparison through LOO and WAIC metrics.

Key Features

01Automated model diagnostics including R-hat, ESS, and divergence checks

02Hierarchical and multilevel model construction with non-centered parameterization

03Advanced MCMC sampling using the No-U-Turn Sampler (NUTS)

040 GitHub stars

05Comprehensive posterior and prior predictive validation workflows

06Model comparison and selection using LOO and WAIC information criteria

Use Cases

01Analyzing grouped or nested data structures through hierarchical modeling

02Quantifying uncertainty in complex linear and logistic regression models

03Forecasting and time series analysis using autoregressive Bayesian processes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pathvanasoftware/iosstoragecleaner scientific-pymc

For use in Claude.ai and ChatGPT

Download Skill