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
Once the model is specified, you run the "Inference Button" by calling pm.sample() . pymc regression tutorial
In PyMC, models are defined within a with pm.Model() as model: context manager. A standard linear regression model ( ) is broken down into three main components: PyMC provides a flexible framework for Bayesian linear