Open AccessApproaches to the construction of nonlinear models in fuzzy environment
Author(s) -
Dilnoz Muhamediyeva,
J Sayfiyev
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 - 10.1088/1742-6596/1260/10/102012
Subject(s) - nonlinear system , bayesian probability , computer science , bayesian linear regression , nonlinear regression , gaussian , mathematics , statistical model , machine learning , algorithm , artificial intelligence , bayesian inference , regression analysis , physics , quantum mechanics
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.