What version of PyMC does this skill support?

This skill is optimized for modern PyMC versions (5.x+), utilizing the latest API features and ArviZ integration for robust statistical analysis.

Can I use this for model comparison?

Yes, the skill provides specific patterns for using ArviZ to compare multiple models using Information Criteria like LOO (Leave-One-Out cross-validation) and WAIC.

How does it handle hierarchical models?

It implements industry best practices for hierarchical modeling, specifically emphasizing non-centered parameterization to avoid sampling divergences in the geometry of the posterior.

Is it compatible with other Python data science libraries?

Yes, it is designed to work seamlessly within the standard Python scientific stack, including NumPy, Pandas, and ArviZ for visualization.

Does it assist with sampling issues like divergences?

Absolutely. It includes detailed diagnostic workflows for identifying and resolving common MCMC issues like divergences, high R-hat values, and low effective sample size (ESS).

PyMC Bayesian Modeling

Name: PyMC Bayesian Modeling
Author: Blurjp

byBlurjp

Data Science & ML

Builds and validates advanced Bayesian statistical models using PyMC and modern probabilistic programming techniques.

About

This skill equips Claude with specialized knowledge for implementing comprehensive Bayesian workflows in Python using the PyMC library. It provides expert guidance on constructing complex hierarchical models, performing MCMC sampling with NUTS, and conducting rigorous model diagnostics via ArviZ. Whether you are dealing with uncertainty quantification, missing data, or multilevel structures, this skill ensures best practices like non-centered parameterization and prior/posterior predictive checks are followed to achieve robust, interpretable inference.

Key Features

Hierarchical and multilevel model construction with non-centered parameterization
Prior and posterior predictive checks for rigorous model validation
0 GitHub stars
Comprehensive model diagnostics including R-hat, ESS, and divergence checking
Advanced MCMC sampling (NUTS) and variational inference (ADVI) implementation
Model comparison and selection using LOO and WAIC criteria

Use Cases

Estimating uncertainty in complex linear and logistic regression models
Analyzing grouped or nested data using hierarchical Bayesian structures
Forecasting and time series analysis using autoregressive processes

About

Key Features

Hierarchical and multilevel model construction with non-centered parameterization
Prior and posterior predictive checks for rigorous model validation
0 GitHub stars
Comprehensive model diagnostics including R-hat, ESS, and divergence checking
Advanced MCMC sampling (NUTS) and variational inference (ADVI) implementation
Model comparison and selection using LOO and WAIC criteria

Use Cases

Estimating uncertainty in complex linear and logistic regression models
Analyzing grouped or nested data using hierarchical Bayesian structures
Forecasting and time series analysis using autoregressive processes