Optimal filters for a hidden Markov random field model

A Markov random field (MRF) is a useful technical tool for modeling dynamics systems exhibiting some type of spatio-temporal variability. In this paper, we propose optimal filters for the states of a partially observed temporal Markov random field. We also discuss parameters estimation. This general...

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Bibliographic Details
Published in:Mathematical and computer modelling 2000-06, Vol.31 (13), p.1-9
Main Authors: Aggoun, L., Benkherouf, L., Benmerzouga, A.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Hidden Markov models
Inference from stochastic processes
time series analysis
Markov processes
Mathematics
Measure change techniques
Modelling and identification
Optimal filtering
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Spatio-Temporal models
Statistics
Stochastic processes
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Summary:A Markov random field (MRF) is a useful technical tool for modeling dynamics systems exhibiting some type of spatio-temporal variability. In this paper, we propose optimal filters for the states of a partially observed temporal Markov random field. We also discuss parameters estimation. This generalizes an earlier work by Elliott and Aggoun [1].
ISSN:0895-7177
1872-9479
DOI:10.1016/S0895-7177(00)00107-2