Pymc Regression Tutorial -

: This connects the model to your observed data. For linear regression, the outcome variable is usually modeled as a Normal distribution: pm.Normal("y", mu=mu, sigma=sigma, observed=y) . 2. Inference and Sampling

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

: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models. : This connects the model to your observed data

Once the model is specified, you run the "Inference Button" by calling pm.sample() . PyMC uses the No-U-Turn Sampler (NUTS)