Cylindrical hidden Markov fields are proposed as a parsimonious strategy to analyze spatial cylindrical data, i.e. bivariate spatial series of angles and intensities. These models are mixtures of copula-based bivariate densities, whose parameters vary across space according to a latent Markov random field. They enable segmentation of spatial cylindrical data within a finite number of latent classes that represent the conditional distributions of the data under specific environmental conditions, simultaneously accounting for spatial auto-correlation.

Lagona, F. (2019). Cylindrical hidden Markov fields. In F.G. GC Porzio (a cura di), Proceedings CLADAG 2019 (pp. 288-291). Cassino : Edizioni Universita' di Cassino.

Cylindrical hidden Markov fields

Francesco Lagona
2019

Abstract

Cylindrical hidden Markov fields are proposed as a parsimonious strategy to analyze spatial cylindrical data, i.e. bivariate spatial series of angles and intensities. These models are mixtures of copula-based bivariate densities, whose parameters vary across space according to a latent Markov random field. They enable segmentation of spatial cylindrical data within a finite number of latent classes that represent the conditional distributions of the data under specific environmental conditions, simultaneously accounting for spatial auto-correlation.
978-88-8317-108-6
Lagona, F. (2019). Cylindrical hidden Markov fields. In F.G. GC Porzio (a cura di), Proceedings CLADAG 2019 (pp. 288-291). Cassino : Edizioni Universita' di Cassino.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/358923
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