: 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
: The sampling process produces a Trace (often stored in an InferenceData object via ArviZ), which contains the posterior samples for every parameter. 3. Posterior Analysis pymc regression tutorial
After sampling, you analyze the results to understand parameter uncertainty. : This connects the model to your observed data
: This is the core formula, typically defined as mu = intercept + slope * x . pymc regression tutorial
Once the model is specified, you run the "Inference Button" by calling pm.sample() .