The Fisher-Rao Metric: Geometric Approach in Probability and Statistics
Fisher Information Matrix, Riemannian Metric, Riemannian Statistical Manifold, Fisher-Rao
Distance, Kullback-Leibler Divergence, Statistical Inference.
In this dissertation, we will see how the Fisher information matrix gives rise to a Riemanian metric
in regular parametric statistical models and how the concept of Riemanian statistical manifold is derived from this. We
will see that this metric provides a measure of dissimilarity between probability distributions, known as the Fisher-Rao
distance. We will show that the parametric family of multivariate Gaussian distributions is a Riemanian statistical
manifold. We will present a relationship between the Fisher-Rao distance and the Kullback-Leibler divergence. Finally,
we will illustrate through examples how tools from Riemannian Geometry can be used in questions related to
Statistical Inference.