Approaches to the construction of nonlinear models in fuzzy environment
D.T. MuhamediyevaTashkent University of Information Technologies named after Muhammad alKhwarizmi, Tashkent, UzbekistanJ SayfiyevTashkent University of Information Technologies named after Muhammad alKhwarizmi, Tashkent, Uzbekistan
ABI
Abstract
Abstract The Bayesian methods to the problems of statistical estimation when building nonlinear models are considered. Bayesian methods can be used to construct linear and nonlinear regression models with non-Gaussian laws for the distribution of probabilities of random observation errors. We consider continuous and discrete processes that can be described by statistical models. For this purpose, a description of computational Bayesian procedures and recommendations for the construction of nonlinear regression models are given introduction.
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