PyMC Bayesian Modeling

关于

The PyMC skill enhances Claude's ability to assist with probabilistic programming and Bayesian inference. It provides specialized knowledge for building complex statistical models, including hierarchical and multilevel structures, while ensuring best practices like non-centered parameterization. By integrating MCMC sampling patterns, variational inference, and ArviZ-based diagnostics, this skill helps users perform robust uncertainty quantification, model comparison, and predictive checks directly within their development workflow.

主要功能

• Comprehensive model diagnostics including R-hat and ESS checks
• 0 GitHub stars
• Posterior and prior predictive checks for model validation
• Optimized MCMC sampling configurations using the NUTS sampler
• Advanced uncertainty quantification and credible interval estimation
• Expert construction of linear, logistic, and hierarchical Bayesian models

使用场景

• Developing hierarchical models to account for group-level variations in data
• Quantifying prediction uncertainty for risk assessment and decision making
• Comparing multiple statistical models using LOO and WAIC metrics