When a classifier starts from class conditional distributions, the decision rule changes from drawing a boundary first to asking how likely each class makes the same observation. When those class patterns are modeled with shared Gaussian structure, the posterior becomes a smooth transformation of a linear score, and the boundary stays linear. The alternative approach, allowing different covariance structure per class, bends that boundary into something more flexible. That shift is the key idea: assumptions about uncertainty quietly determine the geometry of decisions.