Riemannian geometry of probability distributions

This thesis investigates the geometric modeling of probability distributions and the application of Riemannian geometry tools on these geometric models using the Fisher information metric. Serving as an introduction to information geometry, the study encompasses fundamental tools from probability theory and Riemannian geometry. Concepts such as metrics, connections, and curvatures on statistical models are examined. Results obtained specifically for the Rayleigh distribution are also discussed.