Subjects: Mathematics >> Statistics and Probability Subjects: Statistics >> Mathematical Statistics Subjects: Information Science and Systems Science >> Basic Disciplines of Information Science and Systems Science submitted time 2024-04-11
Abstract: Statistical independence is a core concept in statistics and machine learning. Representing and measuring independence are of fundamental importance in related fields. Copula theory provides the tool for representing statistical independence, while Copula Entropy (CE) presents the tool for measuring statistical independence. This paper first introduces the theory of CE, including its definition, theorem, properties, and estimation method. The theoretical applications of CE to structure learning, association discovery, variable selection, causal discovery, system identification, time lag estimation, domain adaptation, multivariate normality test, two-sample test, and change point detection are reviewed. The relationships between the former four applications and their connection to correlation and causality are discussed. The frameworks based on CE, the kernel method, and distance correlation for measuring statistical independence and conditional independence are compared. The advantage of CE over other independence and conditional independence measures is evaluated. The applications of CE in theoretical physics, astrophysics, geophysics, theoretical chemistry, cheminformatics, materials science, hydrology, climatology, meteorology, environmental science, ecology, animal morphology, agronomy, cognitive neuroscience, motor neuroscience, computational neuroscience, psychology, system biology, bioinformatics, clinical diagnostics, geriatrics, psychiatry, public health, economics, management, sociology, pedagogy, computational linguistics, mass media, law, political science, military science, informatics, energy, food engineering, architecture, civil engineering, transportation, manufacturing, reliability, metallurgy, chemical engineering, aeronautics and astronautics, weapon, automobile, electronics, communication, high performance computing, cybersecurity, remote sensing, and finance are briefly introduced.
Peer Review Status:Awaiting Review
Subjects: Mathematics >> Computational Mathematics. Subjects: Information Science and Systems Science >> Basic Disciplines of Information Science and Systems Science submitted time 2020-03-16
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Peer Review Status:Awaiting Review
Subjects: Physics >> Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics Subjects: Information Science and Systems Science >> Basic Disciplines of Information Science and Systems Science Subjects: Mathematics >> Mathematics (General) submitted time 2017-11-26
Abstract: The infrared imaging grayscale variation caused by the influence of atmosphere on infrared radiation transmission is a problem that infrared target tracking application needs to cope with. The object of this paper is to model the law of infrared imaging grayscale variation in Lie group, which is important to design an efficient and robust target tracking algorithm. This paper firstly analyzes the infrared radiation transmission model, and then derives the brightness model of infrared imaging by considering the mechanism of infrared imaging. Furthermore, it is theoretically proved that the infrared imaging grayscale variation caused by the atmosphere obeys to the Lie group structure, and a non-Euclidean mathematical representation of the infrared imaging grayscale variation is proposed. Finally, according to the infrared imaging grayscale variation model, the field experimental data collected under different environments are fitted, and the regression analysis results demonstrate the correctness of the model, which validates the rationality of the Lie group representation of the infrared imaging grayscale variation.
Peer Review Status:Awaiting Review