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
2019-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.