How does it help with hierarchical model sampling issues?

The skill implements non-centered parameterization patterns as a default for hierarchical models to specifically prevent divergences and improve NUTS sampling efficiency.

Does it provide tools for model comparison?

Yes, it includes standardized scripts and workflows for comparing multiple models using Leave-One-Out (LOO) cross-validation and WAIC.

Can this skill handle model diagnostics?

Yes, it includes procedures for checking R-hat, Effective Sample Size (ESS), and trace plots to ensure your MCMC chains have properly converged.

What version of PyMC does this skill support?

This skill is optimized for PyMC version 5.x and above, utilizing the most modern API and sampling features available in the ecosystem.

Does it support Variational Inference?

Yes, it provides patterns for using ADVI (Automatic Differentiation Variational Inference) for quick model exploration and initialization of MCMC samplers.

PyMC Bayesian Modeling

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

byK-Dense-AI

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2,188

Data Science & ML

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

About

This skill empowers Claude to perform advanced probabilistic programming tasks using PyMC 5.x+. It provides a comprehensive framework for the entire Bayesian workflow—from data preparation and prior predictive checks to MCMC sampling with NUTS and complex model diagnostics. Whether you're building hierarchical models, time-series analyses, or conducting model comparisons using LOO/WAIC, this skill ensures best practices like non-centered parameterization and proper uncertainty quantification are followed for reliable scientific inference.

Key Features

Advanced MCMC sampling using the NUTS algorithm and variational inference (ADVI)
2,188 GitHub stars
Hierarchical and multilevel modeling with automated non-centered parameterization
Standardized workflows for prior and posterior predictive checks (PPC)
Comprehensive model diagnostics including R-hat, ESS, and divergence checks
Model comparison and averaging using LOO and WAIC information criteria

Use Cases

Performing robust time-series forecasting and autoregressive modeling
Analyzing grouped or nested data using hierarchical Bayesian frameworks
Quantifying uncertainty in complex linear and logistic regression models

About

Key Features

Advanced MCMC sampling using the NUTS algorithm and variational inference (ADVI)
2,188 GitHub stars
Hierarchical and multilevel modeling with automated non-centered parameterization
Standardized workflows for prior and posterior predictive checks (PPC)
Comprehensive model diagnostics including R-hat, ESS, and divergence checks
Model comparison and averaging using LOO and WAIC information criteria

Use Cases

Performing robust time-series forecasting and autoregressive modeling
Analyzing grouped or nested data using hierarchical Bayesian frameworks
Quantifying uncertainty in complex linear and logistic regression models