Revisiting Chernoff Information with Likelihood Ratio Exponential Families. [PDF]
Entropy (Basel), 2022 The Chernoff information between two probability measures is a statistical divergence measuring their deviation defined as their maximally skewed Bhattacharyya distance.
Nielsen F.
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Statistical Divergences between Densities of Truncated Exponential Families with Nested Supports: Duo Bregman and Duo Jensen Divergences. [PDF]
Entropy (Basel), 2022 By calculating the Kullback–Leibler divergence between two probability measures belonging to different exponential families dominated by the same measure, we obtain a formula that generalizes the ordinary Fenchel–Young divergence.
Nielsen F.
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On Representations of Divergence Measures and Related Quantities in Exponential Families. [PDF]
Entropy (Basel), 2021 Within exponential families, which may consist of multi-parameter and multivariate distributions, a variety of divergence measures, such as the Kullback–Leibler divergence, the Cressie–Read divergence, the Rényi divergence, and the Hellinger metric, can ...
Bedbur S, Kamps U.
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Information Geometric Duality of <i>ϕ</i>-Deformed Exponential Families. [PDF]
Entropy (Basel), 2019 In the world of generalized entropies—which, for example, play a role in physical systems with sub- and super-exponential phase space growth per degree of freedom—there are two ways for implementing constraints in the maximum entropy ...
Korbel J, Hanel R, Thurner S.
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Information Geometry of κ-Exponential Families: Dually-Flat, Hessian and Legendre Structures. [PDF]
Entropy (Basel), 2018 In this paper, we present a review of recent developments on the κ -deformed statistical mechanics in the framework of the information geometry.
Scarfone AM, Matsuzoe H, Wada T.
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Two Families of Continuous Probability Distributions Generated by the Discrete Lindley Distribution
Mathematics, 2023 In this paper, we construct two new families of distributions generated by the discrete Lindley distribution. Some mathematical properties of the new families are derived. Some special distributions from these families can be constructed by choosing some
Srdjan Kadić +2 more
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Networked Exponential Families for Big Data Over Networks
IEEE Access, 2020 The data generated in many application domains can be modeled as big data over networks, i.e., massive collections of high-dimensional local datasets related via an intrinsic network structure.
Alexander Jung
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On Hölder Projective Divergences
Entropy, 2017 We describe a framework to build distances by measuring the tightness of inequalities and introduce the notion of proper statistical divergences and improper pseudo-divergences.
Frank Nielsen +2 more
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Deformed Algebras and Generalizations of Independence on Deformed Exponential Families
Entropy, 2015 A deformed exponential family is a generalization of exponential families. Since the useful classes of power law tailed distributions are described by the deformed exponential families, they are important objects in the theory of complex systems.
Hiroshi Matsuzoe, Tatsuaki Wada
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Exponential dichotomy for evolution families on the real line
Abstract and Applied Analysis, 2006 We give necessary and sufficient conditions for uniform exponential dichotomy of evolution families in terms of the admissibility of the pair (Lp(ℝ,X),Lq(ℝ,X)).
Adina Luminiţa Sasu
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