Optimal filters for a hidden Markov random field model
Markov random fields models are a convenient technical tool for modeling and studying numerous phenomena that occur in real life, in particular where one is interested in processes exhibiting some type of spatio-temporal variability. This paper presents optimal filters for the states of a partially observed temporal Markov random field. The parameter estimation problem is also discussed.
scientific article; zbMATH DE number 1529779