Does it support hierarchical or multilevel modeling?

Yes, it specifically includes patterns for hierarchical models and recommends non-centered parameterization to prevent common sampling issues like divergences.

How does it handle model diagnostics?

The skill provides templates for checking R-hat, Effective Sample Size (ESS), and trace plots using ArviZ to ensure your MCMC chains have successfully converged.

Can I use this for model comparison?

Absolutely. It supports Leave-One-Out (LOO) cross-validation and WAIC for comparing multiple Bayesian models to find the most appropriate fit for your data.

What is the primary benefit of using this PyMC skill?

This skill enforces a rigorous Bayesian workflow, ensuring you don't skip critical steps like prior predictive checks or convergence diagnostics, leading to more reliable statistical results.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: ricable

byricable

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Data Science & ML

Builds and validates complex Bayesian models for probabilistic inference using PyMC and ArviZ.

About

The PyMC Bayesian Modeling skill equips Claude with specialized expertise in probabilistic programming and statistical inference. It facilitates a robust, end-to-end Bayesian workflow, encompassing data standardization, weakly informative prior selection, MCMC sampling via the NUTS algorithm, and rigorous model diagnostics. This skill is particularly valuable for data scientists and researchers needing to implement hierarchical models, quantify uncertainty in predictions, or handle missing data through principled probabilistic methods, ensuring all models adhere to modern PyMC 5.x+ best practices.

Key Features

Model comparison workflows using LOO and WAIC information criteria
Automated model diagnostics for R-hat, ESS, and divergences
Prior and posterior predictive check (PPC) integration
Standardized MCMC sampling using NUTS and variational inference (ADVI)
Hierarchical modeling templates with non-centered parameterization
1 GitHub stars

Use Cases

Implementing uncertainty-aware regression for scientific or financial forecasting
Building hierarchical/multilevel models for grouped data structures
Performing robust model selection and averaging for complex datasets

About

Key Features

Model comparison workflows using LOO and WAIC information criteria
Automated model diagnostics for R-hat, ESS, and divergences
Prior and posterior predictive check (PPC) integration
Standardized MCMC sampling using NUTS and variational inference (ADVI)
Hierarchical modeling templates with non-centered parameterization
1 GitHub stars

Use Cases

Implementing uncertainty-aware regression for scientific or financial forecasting
Building hierarchical/multilevel models for grouped data structures
Performing robust model selection and averaging for complex datasets