informationgeometry
Information geometry is a field that applies differential geometry to probability theory and statistics. It views the set of probability distributions as a manifold, where each point on the manifold corresponds to a specific probability distribution. The geometric structure of this manifold allows for the analysis of statistical models and inference procedures using tools from differential geometry.
Key concepts in information geometry include the Fisher information metric, which defines a distance measure between
Applications of information geometry are found in various areas, including machine learning, signal processing, and Bayesian