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Information geometry is a study of statistical manifolds, that is, spaces ofprobability distributions from a geometric perspective. Its classicalinformation-theoretic applications relate to statistical concepts such asFisher information, sufficient statistics, and efficient estimators. Today,information geometry has emerged as an interdisciplinary field that findsapplications in diverse areas such as radar sensing, array signal processing,quantum physics, deep learning, and optimal transport. This article presents anoverview of essential information geometry to initiate an information theorist,who may be unfamiliar with this exciting area of research. We explain theconcepts of divergences on statistical manifolds, generalized notions ofdistances, orthogonality, and geodesics, thereby paving the way for concreteapplications and novel theoretical investigations. We also highlight somerecent information-geometric developments, which are of interest to the broaderinformation theory community.

Information Geometry for the Working Information Theorist