Pymc - Regression Tutorial

Once the model is specified, you run the "Inference Button" by calling pm.sample() .

: 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 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 Once the model is specified, you run the

PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation Once the model is specified