22.1 Expressing the models so far in a common framework

Much can be done with the basic model:

\[ y = X\beta + \epsilon \]

• Maximum likelihood for point estimation
• Including priors (regularization)
• Sampling the posterior to include uncertainty
• Forecasting including predictive uncertainty
• Additive nonlinear models (e.g. polynomials)