Pymc | Regression Tutorial

: Unlike frequentist confidence intervals, Bayesian credible intervals (e.g., a 94% HDI) provide a direct probability that a parameter falls within a certain range. 4. Advanced Regression Types

: This is the core formula, typically defined as mu = intercept + slope * x . 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. : Unlike frequentist confidence intervals

PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation Bayesian credible intervals (e.g.

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