pymc

What problem does it solve?

This Skill empowers users to build, fit, and validate sophisticated Bayesian statistical models, enabling robust inference and uncertainty quantification for complex data analysis tasks.

Core Features & Use Cases

• Bayesian Modeling: Construct hierarchical models, regression models (linear, logistic, Poisson), and time series models.
• Probabilistic Programming: Leverage PyMC's modern API for MCMC sampling (NUTS) and variational inference.
• Model Validation: Perform prior and posterior predictive checks, analyze diagnostics (R-hat, ESS, divergences), and compare models using LOO/WAIC.
• Use Case: A data scientist needs to model customer behavior with uncertainty. They can use this Skill to build a hierarchical Bayesian model that accounts for individual customer variations while learning population-level trends, providing reliable predictions with confidence intervals.

Quick Start

Use the pymc skill to build a Bayesian linear regression model for the provided data.