Discriminant Analysis under f-Divergence Measures [PDF]
Entropy, 2022 In statistical inference, the information-theoretic performance limits can often be expressed in terms of a statistical divergence between the underlying statistical models (e.g., in binary hypothesis testing, the error probability is related to the ...
Anmol Dwivedi, Sihui Wang, Ali Tajer
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Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data [PDF]
Current Issues in Molecular BiologyInferring time-varying gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data remains a challenging task. The existing methods have notable limitations as most are either designed for reconstructing time-varying networks ...
Yunge Wang +5 more
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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data [PDF]
StatsImputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy ...
Wen-Shan Liu +4 more
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Some $f$-Divergence Measures Related to Jensen's One
Universal Journal of Mathematics and Applications, 2023 In this paper, we introduce some $f$-divergence measures that are related to the Jensen's divergence introduced by Burbea and Rao in 1982. We establish their joint convexity and provide some inequalities between these measures and a combination of Csisz\'
Sever Dragomır
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Quadratic Quasinorm and Its Applications in Risk Analysis
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2021 This paper deals with the estimation of the probability distribution of the category random variable from its observed values. The gradient estimation presented is based on the f-quasi-norm term.
Jakub Šácha +2 more
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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Entropy, 2021 Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit
Chiara Leadbeater +3 more
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On the Converse Jensen-Type Inequality for Generalized f-Divergences and Zipf–Mandelbrot Law
Mathematics, 2022 Motivated by some recent investigations about the sharpness of the Jensen inequality, this paper deals with the sharpness of the converse of the Jensen inequality.
Mirna Rodić
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Some inequalities related to Csiszár divergence via diamond integral on time scales
Journal of Inequalities and Applications, 2023 In this paper, Csiszár f-divergence via diamond integral is introduced and some inequalities related to Csiszár f-divergence involving diamond integrals are presented.
Muhammad Bilal +3 more
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Robust Multiple Importance Sampling with Tsallis φ-Divergences
Entropy, 2022 Multiple Importance Sampling (MIS) combines the probability density functions (pdf) of several sampling techniques. The combination weights depend on the proportion of samples used for the particular techniques.
Mateu Sbert, László Szirmay-Kalos
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Inequalities for Jensen–Sharma–Mittal and Jeffreys–Sharma–Mittal Type f–Divergences
Entropy, 2021 In this paper, we introduce new divergences called Jensen–Sharma–Mittal and Jeffreys–Sharma–Mittal in relation to convex functions. Some theorems, which give the lower and upper bounds for two new introduced divergences, are provided.
Paweł A. Kluza
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