Results 21 to 30 of about 10,752 (214)
Entropy, 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.
Frank Nielsen
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On the Interventional Kullback-Leibler Divergence
CoRR, 2023 Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intelligent agent needs to be able to transfer knowledge and re-use learned components across domains. It has been argued that this may be possible through causal models, aiming to
Wildberger, J. ; https://orcid.org/0000-0002-3433-5920 +3 more
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The Kullback-Leibler Divergence Class in Decoding the Chest Sound Pattern [PDF]
Informatică economică, 2019 Kullback-Leibler Divergence Class or relative entropy is a special case of broader divergence. It represents a calculation of how one probability distribution diverges from another one, expected probability distribution. Kullback-Leibler divergence has a
Antonio CLIM, Razvan Daniel ZOTA
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Chained Kullback-Leibler divergences [PDF]
2016 IEEE International Symposium on Information Theory (ISIT), 2016 We define and characterize the "chained" Kullback-Leibler divergence min w D(p‖w) + D(w‖q) minimized over all intermediate distributions w and the analogous k-fold chained K-L divergence min D(p‖wk-1) + … + D(w2‖w1) + D(w1‖q) minimized over the entire path (w1,…,wk-1).
Dmitri S. Pavlichin, Tsachy Weissman
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Parameter Estimation Based on Cumulative Kullback–Leibler Divergence
Revstat Statistical Journal, 2021 In this paper, we propose some estimators for the parameters of a statistical model based on Kullback–Leibler divergence of the survival function in continuous setting and apply it to type I censored data.
Yaser Mehrali , Majid Asadi
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Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model
Complexity, 2022 In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting ...
Wanbo Lu, Wenhui Shi
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On Voronoi Diagrams on the Information-Geometric Cauchy Manifolds
Entropy, 2020 We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat ...
Frank Nielsen
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Kullback–Leibler Divergence and Mutual Information of Partitions in Product MV Algebras
Entropy, 2017 The purpose of the paper is to introduce, using the known results concerning the entropy in product MV algebras, the concepts of mutual information and Kullback–Leibler divergence for the case of product MV algebras and examine algebraic properties of ...
Dagmar Markechová, Beloslav Riečan
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Entropy, 2016 Information-theoretic measures, such as the entropy, the cross-entropy and the Kullback–Leibler divergence between two mixture models, are core primitives in many signal processing tasks.
Frank Nielsen, Ke Sun
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Some bounds for skewed α-Jensen-Shannon divergence
Results in Applied Mathematics, 2019 Based on the skewed Kullback-Leibler divergence introduced in the natural language processing, we derive the upper and lower bounds on the skewed version of the Jensen-Shannon divergence and investigate properties of them.
Takuya Yamano
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