pymc-bayesian-modeling

What problem does it solve?

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

Core Features & Use Cases

• Bayesian Modeling: Construct hierarchical models, regression, time series, and more using PyMC.
• Probabilistic Programming: Define priors, likelihoods, and custom distributions for flexible modeling.
• Inference Methods: Perform MCMC sampling (NUTS) and Variational Inference (ADVI).
• Diagnostics & Validation: Assess model convergence, fit, and reliability using ArviZ.
• Use Case: A researcher wants to model gene expression data with a hierarchical structure accounting for batch effects. This Skill allows them to define the model, sample from the posterior, check diagnostics, and interpret the results.

Quick Start

Use the pymc-bayesian-modeling skill to build a linear regression model with the provided data.