Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Algebraic Bayesian Networks: the Generation of the Network Canonical Representation. / Харитонов, Никита Алексеевич; Вяткин, Артём Андреевич; Тулупьев, Александр Львович.
Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Том 777 Springer Nature. ред. Springer Nature, 2023. стр. 13-22.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Algebraic Bayesian Networks: the Generation of the Network Canonical Representation
AU - Харитонов, Никита Алексеевич
AU - Вяткин, Артём Андреевич
AU - Тулупьев, Александр Львович
N1 - Conference code: 7
PY - 2023/9/25
Y1 - 2023/9/25
N2 - Algebraic Bayesian networks belong to the class of probabilistic graphical models. They are represented as non-directional graphs with models of knowledge patterns in the nodes (KP). Each knowledge pattern contains closely related information about the subject domain, formalized in the form of an ideal of conjuncts or a set of quanta with truth probability estimates. In order to optimize the complexity of KPs through the use of scalar estimates, an approach to finding the canonical representation of KPs has previously been proposed. In this paper, the process of obtaining a canonical representation of the entire algebraic Bayesian network is proposed and studied for the first time. As a result, methods have been described that create a canonical representation based on the comprehensive KP and using chain generation. The results of this paper allow to reduce the time for calculating probability estimates in a priori inference by obtaining scalar estimates instead of interval estimates, which can be used to compute prior probability estimates or in systems where obtaining scalar probability estimates is preferable.
AB - Algebraic Bayesian networks belong to the class of probabilistic graphical models. They are represented as non-directional graphs with models of knowledge patterns in the nodes (KP). Each knowledge pattern contains closely related information about the subject domain, formalized in the form of an ideal of conjuncts or a set of quanta with truth probability estimates. In order to optimize the complexity of KPs through the use of scalar estimates, an approach to finding the canonical representation of KPs has previously been proposed. In this paper, the process of obtaining a canonical representation of the entire algebraic Bayesian network is proposed and studied for the first time. As a result, methods have been described that create a canonical representation based on the comprehensive KP and using chain generation. The results of this paper allow to reduce the time for calculating probability estimates in a priori inference by obtaining scalar estimates instead of interval estimates, which can be used to compute prior probability estimates or in systems where obtaining scalar probability estimates is preferable.
KW - machine learning
KW - probabilistic graphical models
KW - algebraic Bayesian networks
KW - knowledge pattern
KW - Monte Carlo method
KW - canonical representation
KW - machine learning
KW - probabilistic graphical models
KW - algebraic Bayesian networks
KW - knowledge pattern
KW - Monte Carlo method
KW - canonical representation
U2 - 10.1007/978-3-031-43792-2_2
DO - 10.1007/978-3-031-43792-2_2
M3 - Conference contribution
SN - 978-3-031-43788-5
VL - 777
SP - 13
EP - 22
BT - Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23)
PB - Springer Nature
T2 - Seventh International Scientific Conference “Intelligent Information Technologies for Industry”
Y2 - 25 September 2023 through 30 September 2023
ER -
ID: 116496518