Markov Random Field Extensions using State Space Models
نویسندگان
چکیده
We elaborate on the link between state space models and (Gaussian) Markov random fields. We extend the Markov random field models by generalising the corresponding state space model. It turns out that several non Gaussian spatial models can be analysed by combining approximate Kalman filter techniques with importance sampling. We illustrate the ideas by formulating a model for edge detection in digital images, which then forms the basis of a simulation study.
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