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Special Issue "Information Geometry II"
Deadline for manuscript submissions: closed (30 April 2017).
Prof. Dr. Geert Verdoolaege Website E-Mail
1. Research Unit Nuclear Fusion, Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
2. Laboratory for Plasma Physics, Royal Military Academy (ERM/KMS), Renaissancelaan 30 Avenue de la Renaissance, B-1000 Brussels, Belgium
Interests: probability theory; Bayesian inference; machine learning; information geometry; differential geometry; nuclear fusion; plasma physics; plasma turbulence; continuum mechanics; statistical mechanics
The mathematical field of Information Geometry originated from the observation by C.R. Rao in 1945 the Fisher information can be used to define a Riemannian metric in spaces of probability distributions. This led to a geometrical description of probability theory and statistics, allowing the study of the invariant properties of statistical manifolds. It was through the work of S.-I. Amari and others that it was later realized that the differential-geometric structure of a statistical manifold can be derived from divergence functions, yielding a Riemannian metric and a pair of dually coupled affine connections.
Since then, Information Geometry has become a truly interdisciplinary field with applications in various domains. It enables a deeper understanding of the methods of statistical inference and machine learning, while providing a powerful framework for deriving new algorithms. As such, Information Geometry has many applications in optimization (e.g., on matrix manifolds), signal and image processing, computer vision, neural networks and other subfields of the information sciences. Furthermore, the methods of Information Geometry have been applied to a wide variety of topics in physics, mathematical finance, biology and the neurosciences. In physics, there are many links with fields that have a natural probabilistic interpretation, including (non-extensive) statistical mechanics and quantum mechanics.
For this Special Issue we welcome submissions related to the foundations and applications of Information Geometry. We envisage contributions that aim at clarifying the connection of Information Geometry with both the information sciences and the physical sciences, so as to demonstrate the profound impact of the field in these disciplines. In addition, we hope to receive original papers illustrating the wide variety of applications of the methods of Information Geometry.
Prof. Dr. Geert Verdoolaege
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Information geometry
- statistical manifold
- probability theory
- machine learning
- signal processing
- image processing
- statistical mechanics
- quantum mechanics