A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks

DOI10.1016/j.ins.2019.01.080zbMath1454.62161OpenAlexW2916561051WikidataQ128386210 ScholiaQ128386210MaRDI QIDQ2215112

Concha Bielza, Shogo Kato, Pedro Larrañaga, Ignacio Leguey

Publication date: 10 December 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.01.080

zbMATH Keywords

dependence measuresdirectional statisticscircular-linear mutual informationtree-structured Bayesian network

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Inference from spatial processes (62M30) Measures of information, entropy (94A17)

Related Items

Recent advances in directional statistics

Uses Software

• CircStats
• circular
• TETRAD

Cites Work

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• A distribution for a pair of unit vectors generated by Brownian motion
• The multivariate normal distribution
• Estimating the dimension of a model
• Bayesian network classifiers
• Möbius transformation and Cauchy parameter estimation
• Topics in Circular Statistics
• On the dimension and entropy of probability distributions
• On measures of dependence
• A multivariate von mises distribution with applications to bioinformatics
• Bivariate models for dependence of angular observations and a related Markov process
• Some Angular-Linear Distributions and Related Regression Models
• A Möbius transformation-induced distribution on the torus
• Some Fundamental Properties of a Multivariate von Mises Distribution
• Approximating discrete probability distributions with dependence trees
• On a Measure of Stochastic Dependence

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