Information geometry refers to an interdisciplinary field that studies probability theory and statistics by using methods of differential geometry. It is a general framework of dual affine connected with Riemannian manifolds. Information geometry involves the study of decision-making by using geometry which also includes modeling-fitting and pattern matching. It offers a novel approach that can be applied in many fields including physical and information sciences. Its primary goal is to research the geometrical structures that can be presented in the manifold connected with a set of probability distributions of a statistical model. Information geometry is also used to measure the degree of information integration among various terminals of a causal dynamical system. This book aims to shed light on the theory and applications of information geometry. It will also provide interesting topics for research, which interested readers can take up. This book is a resource guide for experts as well as students.