Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions

Conference paper

DOI: 10.1007/978-3-319-44636-3_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9868)
Cite this paper as:
Leguey I., Bielza C., Larrañaga P. (2016) Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. In: Luaces O. et al. (eds) Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science, vol 9868. Springer, Cham

Abstract

Modelling the relationship between directional variables is a nearly unexplored field. The bivariate wrapped Cauchy distribution has recently emerged as the first closed family of bivariate directional distributions (marginals and conditionals belong to the same family). In this paper, we introduce a tree-structured Bayesian network suitable for modelling directional data with bivariate wrapped Cauchy distributions. We describe the structure learning algorithm used to learn the Bayesian network. We also report some simulation studies to illustrate the algorithms including a comparison with the Gaussian structure learning algorithm and an empirical experiment on real morphological data from juvenile rat somatosensory cortex cells.

Keywords

Directional statistics Wrapped Cauchy distribution Tree-structure Bayesian networks 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ignacio Leguey
    • 1
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del Monte, MadridSpain

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