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

Leguey Vitoriano, Ignacio and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción and Kato, Shogo (2019). A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. "Information Sciences", v. 486 ; pp. 240-253. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2019.01.080.

Description

Title: A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks
Author/s:
  • Leguey Vitoriano, Ignacio
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
  • Kato, Shogo
Item Type: Article
Título de Revista/Publicación: Information Sciences
Date: June 2019
ISSN: 0020-0255
Volume: 486
Subjects:
Freetext Keywords: Circular-linear mutual information; Tree-structured Bayesian network; Dependence measures; Directional statistics
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Circular data jointly observed with linear data are common in various disciplines. Since circular data require different techniques than linear data, it is often misleading to use usual dependence measures for joint data of circular and linear observations. Moreover, although a mutual information measure between circular variables exists, the measure has drawbacks in that it is defined only for a bivariate extension of the wrapped Cauchy distribution and has to be approximated using numerical methods. In this paper, we introduce two measures of dependence, namely, (i) circular-linear mutual information as a measure of dependence between circular and linear variables and (ii) circular-circular mutual information as a measure of dependence between two circular variables. It is shown that the expression for the proposed circular-linear mutual information can be greatly simplified for a subfamily of Johnson–Wehrly distributions. We apply these two dependence measures to learn a circular-linear tree-structured Bayesian network that combines circular and linear variables. To illustrate and evaluate our proposal, we perform experiments with simulated data. We also use a real meteorological data set from different European stations to create a circular-linear tree-structured Bayesian network model.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain Project
Madrid Regional GovernmentS2013/ICE-2845CASI-CAM-CMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes
Government of SpainFPU13/01941UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2016-796842-PUnspecifiedUnspecifiedUnspecified

More information

Item ID: 64208
DC Identifier: http://oa.upm.es/64208/
OAI Identifier: oai:oa.upm.es:64208
DOI: 10.1016/j.ins.2019.01.080
Official URL: https://www.sciencedirect.com/science/article/pii/S0020025519300581?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 29 Oct 2020 05:57
Last Modified: 29 Oct 2020 06:02
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