Standard

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). Vol. 777 Springer Nature. ed. Springer Nature, 2023. p. 13-22.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Харитонов, НА, Вяткин, АА & Тулупьев, АЛ 2023, Algebraic Bayesian Networks: the Generation of the Network Canonical Representation. in Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Springer Nature edn, vol. 777, Springer Nature, pp. 13-22, Seventh International Scientific Conference “Intelligent Information Technologies for Industry” , Санкт-Петербург, Russian Federation, 25/09/23. https://doi.org/10.1007/978-3-031-43792-2_2

APA

Харитонов, Н. А., Вяткин, А. А., & Тулупьев, А. Л. (2023). Algebraic Bayesian Networks: the Generation of the Network Canonical Representation. In Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23) (Springer Nature ed., Vol. 777, pp. 13-22). Springer Nature. https://doi.org/10.1007/978-3-031-43792-2_2

Vancouver

Харитонов НА, Вяткин АА, Тулупьев АЛ. Algebraic Bayesian Networks: the Generation of the Network Canonical Representation. In Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Springer Nature ed. Vol. 777. Springer Nature. 2023. p. 13-22 https://doi.org/10.1007/978-3-031-43792-2_2

Author

Харитонов, Никита Алексеевич ; Вяткин, Артём Андреевич ; Тулупьев, Александр Львович. / Algebraic Bayesian Networks: the Generation of the Network Canonical Representation. Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Vol. 777 Springer Nature. ed. Springer Nature, 2023. pp. 13-22

BibTeX

@inproceedings{d2ce29f32ce444789651739a1974a7eb,
title = "Algebraic Bayesian Networks: the Generation of the Network Canonical Representation",
abstract = "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.",
keywords = "machine learning, probabilistic graphical models, algebraic Bayesian networks, knowledge pattern, Monte Carlo method, canonical representation, machine learning, probabilistic graphical models, algebraic Bayesian networks, knowledge pattern, Monte Carlo method, canonical representation",
author = "Харитонов, {Никита Алексеевич} and Вяткин, {Артём Андреевич} and Тулупьев, {Александр Львович}",
year = "2023",
month = sep,
day = "25",
doi = "10.1007/978-3-031-43792-2_2",
language = "English",
isbn = "978-3-031-43788-5",
volume = "777",
pages = "13--22",
booktitle = "Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI{\textquoteright}23)",
publisher = "Springer Nature",
address = "Germany",
edition = "Springer Nature",
note = "Seventh International Scientific Conference “Intelligent Information Technologies for Industry” ; Conference date: 25-09-2023 Through 30-09-2023",
url = "https://link.springer.com/book/10.1007/978-3-031-43789-2, https://iiti.rgups.ru/en/",

}

RIS

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