Pymc Regression Tutorial May 2026
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. pymc regression tutorial
: Tools like ArviZ allow you to plot posterior distributions or trace plots to check for convergence. observed=y) . 2. Inference and Sampling
: This is the core formula, typically defined as mu = intercept + slope * x . pymc regression tutorial
: This connects the model to your observed data. For linear regression, the outcome variable is usually modeled as a Normal distribution: pm.Normal("y", mu=mu, sigma=sigma, observed=y) . 2. Inference and Sampling