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Lagrangian Submanifolds of Symplectic Structures Induced by Divergence Functions.

Marco Favretti
Published in: Entropy (Basel, Switzerland) (2020)
Divergence functions play a relevant role in Information Geometry as they allow for the introduction of a Riemannian metric and a dual connection structure on a finite dimensional manifold of probability distributions. They also allow to define, in a canonical way, a symplectic structure on the square of the above manifold of probability distributions, a property that has received less attention in the literature until recent contributions. In this paper, we hint at a possible application: we study Lagrangian submanifolds of this symplectic structure and show that they are useful for describing the manifold of solutions of the Maximum Entropy principle.
Keyphrases
  • systematic review
  • working memory
  • high resolution
  • health information
  • mass spectrometry