: 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
: This is the core formula, typically defined as mu = intercept + slope * x . pymc regression tutorial
PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation : The sampling process produces a Trace (often
: You assign probability distributions to unknown parameters like the intercept ( ), slope ( ), and error ( ). Common choices include: pm.Normal for regression coefficients. pm.HalfNormal or pm.HalfCauchy for the standard deviation ( ) to ensure it remains positive. In PyMC, models are defined within a with pm
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 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
