SPRINGER launches the Journal "Information Geometry"
The journal Information Geometry has taken up the challenge of how to think about and to look at mathematical science.
In principle, Information Geometry can connect various branches of mathematical sciences to allow for uncertainty from geometric thinking. There is still great potential for exploring new paradigms to break through conventional notions. The journal will publish papers on such research along with those on application of information geometry, broadly construed, emphasizing both theoretical and computational aspects.
Topics of interests will include, but not be limited to, the Fisher–Rao metric, dual connections, divergence functions, entropy/cross-entropy, Hessian geometry, exponential/mixture geodesics and projections, Q-statistics, quantum statistical inference and computation, computational information geometry, algebraic statistics, optimal transportation problems, deep neural networks, and related topics.
The authors and audience of the journal will be interdisciplinary, coming from mathematics, statistics, machine learning, statistical and quantum physics, information theory, control theory, neural computation, complex networks, cognitive science, and allied disciplines.