Open AccessUsing probabilistic statistics to determine the parameters of doubly stochastic models based on autoregression with multiple roots
Author(s) -
Konstantin Vasiliev,
V. E. Dementyiev,
Nikita Andriyanov
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1368/3/032019
Subject(s) - autoregressive model , probabilistic logic , property (philosophy) , statistical model , identification (biology) , stochastic modelling , task (project management) , bayesian vector autoregression , computer science , mathematics , image (mathematics) , mathematical statistics , algorithm , artificial intelligence , statistics , bayesian probability , philosophy , botany , management , epistemology , economics , biology
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.