In order to analyze and extract different structural properties of distributions, one can introduce different coordinate systems over the manifold of distributions. In Evolutionary Computation, the Walsh bases and the Building Block Bases are often used to describe populations, which simplifies the analysis of evolutionary operators applying on populations. Quite independent from these approaches, information geometry has been developed as a geometric way to analyze different order dependencies between random variables (e.g., neural activations or genes). In these notes I briefly review the essentials of various coordinate bases and of information geometry. The goal is to give an overview and make the approaches comparable. Besides introducing meaningful coordinate bases, information geometry also offers an explicit way to distinguish different order interactions and it offers a geometric view on the manifold and thereby also on operators that apply on the manifold. For instance, uniform crossover can be interpreted as an orthogonal projection of a population along an m-geodesic, monotonously reducing the theta-coordinates that describe interactions between genes.