Spatial modelling for mixed-state observations
Abstract
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.
Cite
@article{arxiv.0801.2231,
title = {Spatial modelling for mixed-state observations},
author = {Cécile Hardouin and Jian-Feng Yao},
journal= {arXiv preprint arXiv:0801.2231},
year = {2008}
}
Comments
Published in at http://dx.doi.org/10.1214/08-EJS173 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)