After sampling, you analyze the results to understand parameter uncertainty.
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 pymc regression tutorial
: Unlike frequentist confidence intervals, Bayesian credible intervals (e.g., a 94% HDI) provide a direct probability that a parameter falls within a certain range. 4. Advanced Regression Types After sampling, you analyze the results to understand