Abstract
We consider a general modelling for mixed-state data. Such data consist of two components of different types: the observations record many zeros, together with continuous real values. They occur in many application fields, such as rainfall measures or motion analysis from image sequences. The aim of this work is to present ad hoc spatio-temporal models for these data. More precisely, we present a Markov chain of Markov fields modelling, the Markovian fields being defined as mixed-state auto-models, whose local conditional distributions belong to an exponential family and the observations derive from mixed-states variables. Some specific examples are given as well as some preliminary experiments.
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