Using probabilistic statistics to determine the parameters of doubly stochastic models based on autoregression with multiple roots

An important task when describing images using mathematical models is the identification of parameters from an real image. Such a task is of particular difficulty for combined mathematical models, which, among other things, make it possible to describe multidimensional spatially inhomogeneous signal...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1368 (3), p.32019
Main Authors: Vasiliev, K K, Dementyiev, V E, Andriyanov, N A
Format: Article
Language:English
Subjects:
Algorithms
Eigenvalues
Eigenvectors
Isotropy
Mathematical models
Parameter identification
Physics
Probability theory
Regression analysis
Statistical analysis
Stochastic models
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Summary:An important task when describing images using mathematical models is the identification of parameters from an real image. Such a task is of particular difficulty for combined mathematical models, which, among other things, make it possible to describe multidimensional spatially inhomogeneous signals. An example of such a model is a doubly stochastic model based on autoregression with multiple roots of characteristic equations, which has an important property of quasi-isotropy and allows describing a smooth change in the statistical and correlation properties of simulated images. This work is aimed at developing algorithms for identifying the parameters of this model based on the estimated probabilistic properties of the available image.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1368/3/032019