Cylindrical hidden Markov fields

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