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Mathematics - Geometry & Topology | Information Geometry

Information Geometry

Information Geometry

Editor-in-Chief: Shinto Eguchi
Co-Editors: N. Ay; F. Nielsen; J. Zhang

ISSN: 2511-2481 (print version)
ISSN: 2511-249X (electronic version)

Journal no. 41884

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This journal, as the first to be dedicated to the interdisciplinary field of information geometry:

  • Embraces the challenge of uncovering and synthesizing mathematical foundations of information science;
  • Offers a platform for intellectual engagements with overlapping interests and diverse backgrounds in mathematical science;
  • Balances both theoretical and computational approaches, with ample attention to applications;
  • Covers investigations of core concepts defining and studying invariance principles such as the Fisher–Rao metric, dual connection, divergence functions, exponential and mixture geodesics, information projections, and many more areas.

The journal engages its readership in geometrizing the science of information. It connects diverse branches of mathematical sciences that deal with probability, entropy, measurement, inference, and related concepts. Coverage includes original work and synthesis exploring the foundation and application of information geometry in both mathematical and computational aspects.

Related subjects » Geometry & Topology

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  • Aims and Scope

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    This journal will publish original work in the emerging interdisciplinary field of information geometry, with both a theoretical and computational emphasis. Information geometry connects various branches of mathematical science in dealing with uncertainty and information based on unifying geometric concepts. Furthermore, it demonstrates the great potential of abstract thinking and corresponding formalisms within many application fields.

    Theoretical topics of interest will include, but are not limited to, the Fisher–Rao metric, the Amari–Chentsov tensor, alpha geometry, dual connections, exponential and mixture geodesics, divergence functions, information and entropy functions, convex analysis, Hessian geometry, information projections, q-statistics and deformed exponential/logarithm, algebraic statistics, optimal transport geometry, and related topics.

    The authors and audience of this journal will be interdisciplinary, coming from the many disciplines that inspire the development of information-geometric methods and benefit from their application, including mathematics, statistics, machine learning, neuroscience, information theory, statistical and quantum physics, control theory and optimization, complex networks and systems, theoretical biology, cognitive science, mathematical finance, and allied disciplines.

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