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:

After sampling, you analyze the results to understand parameter uncertainty. pymc regression tutorial

: 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