How does it handle sampling issues like divergences?

The skill provides diagnostic workflows to identify divergences and recommends solutions such as increasing target_accept or switching to non-centered parameterization.

What version of PyMC does this skill support?

This skill is optimized for PyMC version 5.x and above, leveraging the modern API and the latest ArviZ integration for posterior analysis.

Can I use this for model comparison?

Yes, it includes specialized methods for comparing multiple models using Leave-One-Out (LOO) cross-validation and Widely Applicable Information Criterion (WAIC).

Does it support large-scale models?

While it supports MCMC for high-dimensional models, it also provides Variational Inference (ADVI) patterns for faster exploration and initialization of large datasets.

Does it help with hierarchical modeling?

Absolutely. It contains specific templates and best-practice guidance for building multilevel models, ensuring proper distribution selection and parameterization.

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: Sologa

bySologa

Data Science & ML

Streamlines the creation, fitting, and validation of Bayesian models using PyMC's modern probabilistic programming interface.

About

The PyMC Bayesian Modeling skill provides a comprehensive toolkit for building robust statistical models within Claude Code, automating the implementation of hierarchical structures, MCMC sampling, and variational inference. It guides developers through the entire Bayesian workflow—from prior predictive checks to sophisticated model comparisons using LOO and WAIC—ensuring that statistical inferences are both accurate and reliable. This skill is particularly useful for data scientists and researchers who need to quantify uncertainty, handle missing data, or implement complex multilevel models with best-practice patterns like non-centered parameterization.

Key Features

Model comparison and selection using information criteria like LOO and WAIC
Built-in templates for hierarchical, linear, and logistic regression models
Advanced MCMC sampling using the NUTS (No-U-Turn Sampler) algorithm
0 GitHub stars
Comprehensive model diagnostics including R-hat, ESS, and divergence tracking
Automated prior and posterior predictive checks for model validation

Use Cases

Quantifying uncertainty in complex hierarchical or multilevel data structures
Implementing Bayesian inference for datasets with missing values or measurement errors
Building robust predictive models for time series, count data, or binary outcomes

About

Key Features

Model comparison and selection using information criteria like LOO and WAIC
Built-in templates for hierarchical, linear, and logistic regression models
Advanced MCMC sampling using the NUTS (No-U-Turn Sampler) algorithm
0 GitHub stars
Comprehensive model diagnostics including R-hat, ESS, and divergence tracking
Automated prior and posterior predictive checks for model validation

Use Cases

Quantifying uncertainty in complex hierarchical or multilevel data structures
Implementing Bayesian inference for datasets with missing values or measurement errors
Building robust predictive models for time series, count data, or binary outcomes