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Geometric Structures of Information
Warped Riemannian Metrics for Location-Scale Models
other
Author(s):
Salem Said
,
Lionel Bombrun
,
Yannick Berthoumieu
Publication date
(Online):
November 20 2018
Publisher:
Springer International Publishing
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Natural Gradient Works Efficiently in Learning
Shun-ichi Amari
(1998)
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Optimization Methods for Large-Scale Machine Learning
Jorge Nocedal
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Léon Bottou
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Book Chapter
Publication date (Print):
2019
Publication date (Online):
November 20 2018
Pages
: 251-296
DOI:
10.1007/978-3-030-02520-5_10
SO-VID:
85f145b5-7964-4e2b-8e56-84986aa1dd8f
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Book chapters
pp. 1
Rho-Tau Embedding of Statistical Models
pp. 15
A Class of Non-parametric Deformed Exponential Statistical Models
pp. 37
Statistical Manifolds Admitting Torsion and Partially Flat Spaces
pp. 51
Conformal Flattening on the Probability Simplex and Its Applications to Voronoi Partitions and Centroids
pp. 69
Monte Carlo Information-Geometric Structures
pp. 105
Information Geometry in Portfolio Theory
pp. 137
Generalising Frailty Assumptions in Survival Analysis: A Geometric Approach
pp. 149
Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence
pp. 213
Information-Theoretic Matrix Inequalities and Diffusion Processes on Unimodular Lie Groups
pp. 251
Warped Riemannian Metrics for Location-Scale Models
pp. 297
Clustering in Hilbert’s Projective Geometry: The Case Studies of the Probability Simplex and the Elliptope of Correlation Matrices
pp. 333
Jean-Louis Koszul and the Elementary Structures of Information Geometry
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