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

: 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. pymc regression tutorial

: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models. Common choices include: 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: In PyMC, models are defined within a with pm

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

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