Pymc Regression Tutorial 🎯 Original

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

After sampling, you analyze the results to understand parameter uncertainty. : You assign probability distributions to unknown parameters

PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation slope ( )

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