Information Geometry

Information geometry treats a family of probability distributions as a manifold, with the Fisher information as its metric. Statistical questions then become geometric ones: an estimator is a point, its efficiency is a curvature statement, and the divergence between two models is a distance that is not symmetric. Writing here uses it where estimation and geometry meet, and where the shape of a model family explains what the model can learn.