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