: The sampling process produces a Trace (often stored in an InferenceData object via ArviZ), which contains the posterior samples for every parameter. 3. Posterior Analysis
PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation pymc regression tutorial
PyMC provides a flexible framework for Bayesian linear regression, allowing you to model data by defining prior knowledge and likelihood functions. Unlike frequentist approaches that find a single "best" set of coefficients, PyMC generates a distribution of possible parameters (the posterior) using Markov Chain Monte Carlo (MCMC) sampling. 1. Model Definition : The sampling process produces a Trace (often