  In several application fields like daily pluviometry data modelling, or
motion analysis from image sequences, observations contain two components of
different nature. A first part is made with discrete values accounting for some
symbolic information and a second part records a continuous (real-valued)
measurement. We call such type of observations "mixed-state observations". This
paper introduces spatial models suited for the analysis of these kinds of data.
We consider multi-parameter auto-models whose local conditional distributions
belong to a mixed state exponential family. Specific examples with exponential
distributions are detailed, and we present some experimental results for
modelling motion measurements from video sequences.
