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:
After sampling, you analyze the results to understand parameter uncertainty. pymc regression tutorial
: 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 In PyMC, models are defined within a with pm
: 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 Bayesian credible intervals (e.g.
: 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
Once the model is specified, you run the "Inference Button" by calling pm.sample() .
PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation