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Geometric structures of information
~
Nielsen, Frank.
Geometric structures of information
Record Type:
Electronic resources : Monograph/item
Title/Author:
Geometric structures of informationedited by Frank Nielsen.
other author:
Nielsen, Frank.
Published:
Cham :Springer International Publishing :2019.
Description:
viii, 392 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Geometrical models in statistics.
Online resource:
https://doi.org/10.1007/978-3-030-02520-5
ISBN:
9783030025205$q(electronic bk.)
Geometric structures of information
Geometric structures of information
[electronic resource] /edited by Frank Nielsen. - Cham :Springer International Publishing :2019. - viii, 392 p. :ill., digital ;24 cm. - Signals and communication technology,1860-4862. - Signals and communication technology..
Rho-Tau Embedding of Statistical Models -- A class of non-parametric deformed exponential statistical models -- Statistical Manifolds Admitting Torsion and Partially Flat Spaces -- Conformal attening on the probability simplex and its applications to Voronoi partitions and centroids Atsumi Ohara -- Monte Carlo Information-Geometric Structures -- Information geometry in portfolio theory -- Generalising Frailty Assumptions in Survival Analysis: a Geometric Approach -- Some Universal Insights on Divergences for Statistics, Machine Learning and Articial Intelligence -- Information-Theoretic Matrix Inequalities and Diusion Processes on Unimodular Lie Groups.
This book focuses on information geometry manifolds of structured data/information and their advanced applications featuring new and fruitful interactions between several branches of science: information science, mathematics and physics. It addresses interrelations between different mathematical domains like shape spaces, probability/optimization & algorithms on manifolds, relational and discrete metric spaces, computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, and applications like geometries of audio-processing, inverse problems and signal processing.The book collects the most important contributions to the conference GSI'2017 - Geometric Science of Information.
ISBN: 9783030025205$q(electronic bk.)
Standard No.: 10.1007/978-3-030-02520-5doiSubjects--Topical Terms:
791341
Geometrical models in statistics.
LC Class. No.: QA276.23 / .G466 2019
Dewey Class. No.: 519.5
Geometric structures of information
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Rho-Tau Embedding of Statistical Models -- A class of non-parametric deformed exponential statistical models -- Statistical Manifolds Admitting Torsion and Partially Flat Spaces -- Conformal attening on the probability simplex and its applications to Voronoi partitions and centroids Atsumi Ohara -- Monte Carlo Information-Geometric Structures -- Information geometry in portfolio theory -- Generalising Frailty Assumptions in Survival Analysis: a Geometric Approach -- Some Universal Insights on Divergences for Statistics, Machine Learning and Articial Intelligence -- Information-Theoretic Matrix Inequalities and Diusion Processes on Unimodular Lie Groups.
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This book focuses on information geometry manifolds of structured data/information and their advanced applications featuring new and fruitful interactions between several branches of science: information science, mathematics and physics. It addresses interrelations between different mathematical domains like shape spaces, probability/optimization & algorithms on manifolds, relational and discrete metric spaces, computational and Hessian information geometry, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, and applications like geometries of audio-processing, inverse problems and signal processing.The book collects the most important contributions to the conference GSI'2017 - Geometric Science of Information.
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Engineering (Springer-11647)
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EB QA276.23 G345 2019 2019
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https://doi.org/10.1007/978-3-030-02520-5
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