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Algebraic Bayesian Networks : Empirical Estimates of the Sensitivity of Local Posteriori Inference. / Zavalishin, A. D.; Tulupyev, A. L.; Maksimov, A. G.

Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020. ред. / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2020. стр. 31-33 9198792 (Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование

Harvard

Zavalishin, AD, Tulupyev, AL & Maksimov, AG 2020, Algebraic Bayesian Networks: Empirical Estimates of the Sensitivity of Local Posteriori Inference. в S Shaposhnikov (ред.), Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020., 9198792, Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020, Institute of Electrical and Electronics Engineers Inc., стр. 31-33, 23rd International Conference on Soft Computing and Measurements, SCM 2020, St. Petersburg, Российская Федерация, 27/05/20. https://doi.org/10.1109/SCM50615.2020.9198792

APA

Zavalishin, A. D., Tulupyev, A. L., & Maksimov, A. G. (2020). Algebraic Bayesian Networks: Empirical Estimates of the Sensitivity of Local Posteriori Inference. в S. Shaposhnikov (Ред.), Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020 (стр. 31-33). [9198792] (Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCM50615.2020.9198792

Vancouver

Zavalishin AD, Tulupyev AL, Maksimov AG. Algebraic Bayesian Networks: Empirical Estimates of the Sensitivity of Local Posteriori Inference. в Shaposhnikov S, Редактор, Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. стр. 31-33. 9198792. (Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020). https://doi.org/10.1109/SCM50615.2020.9198792

Author

Zavalishin, A. D. ; Tulupyev, A. L. ; Maksimov, A. G. / Algebraic Bayesian Networks : Empirical Estimates of the Sensitivity of Local Posteriori Inference. Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020. Редактор / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2020. стр. 31-33 (Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020).

BibTeX

@inproceedings{9d1b04d9b8c14ff7b48b89d22dbf2416,
title = "Algebraic Bayesian Networks: Empirical Estimates of the Sensitivity of Local Posteriori Inference",
abstract = "The article is aimed at studying empirical estimates of the sensitivity of the second task of a posteriori inference in a knowledge pattern. The article presents the results of an experiment on finding the relationship between the distortion of incoming information and the results of learning a knowledge pattern. Formally, information distortions were achieved by changing the estimates of the fixed evidence and finding the norm of the difference between the vectors of the original and the resulting evidence. Obtaining empirical estimates is the first example of studying the second task of a posteriori inference in a knowledge pattern. The relevance of the study is emphasized by the growing popularity of machine learning and, most importantly, data preparation, since ABS is one of the few models that can work with inaccurate data.",
keywords = "algebraic Bayesian networks, knowledge pattern, logical and probabilistic graphical models, machine learning",
author = "Zavalishin, {A. D.} and Tulupyev, {A. L.} and Maksimov, {A. G.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 23rd International Conference on Soft Computing and Measurements, SCM 2020 ; Conference date: 27-05-2020 Through 29-05-2020",
year = "2020",
month = may,
doi = "10.1109/SCM50615.2020.9198792",
language = "English",
series = "Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "31--33",
editor = "S. Shaposhnikov",
booktitle = "Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020",
address = "United States",

}

RIS

TY - GEN

T1 - Algebraic Bayesian Networks

T2 - 23rd International Conference on Soft Computing and Measurements, SCM 2020

AU - Zavalishin, A. D.

AU - Tulupyev, A. L.

AU - Maksimov, A. G.

N1 - Publisher Copyright: © 2020 IEEE.

PY - 2020/5

Y1 - 2020/5

N2 - The article is aimed at studying empirical estimates of the sensitivity of the second task of a posteriori inference in a knowledge pattern. The article presents the results of an experiment on finding the relationship between the distortion of incoming information and the results of learning a knowledge pattern. Formally, information distortions were achieved by changing the estimates of the fixed evidence and finding the norm of the difference between the vectors of the original and the resulting evidence. Obtaining empirical estimates is the first example of studying the second task of a posteriori inference in a knowledge pattern. The relevance of the study is emphasized by the growing popularity of machine learning and, most importantly, data preparation, since ABS is one of the few models that can work with inaccurate data.

AB - The article is aimed at studying empirical estimates of the sensitivity of the second task of a posteriori inference in a knowledge pattern. The article presents the results of an experiment on finding the relationship between the distortion of incoming information and the results of learning a knowledge pattern. Formally, information distortions were achieved by changing the estimates of the fixed evidence and finding the norm of the difference between the vectors of the original and the resulting evidence. Obtaining empirical estimates is the first example of studying the second task of a posteriori inference in a knowledge pattern. The relevance of the study is emphasized by the growing popularity of machine learning and, most importantly, data preparation, since ABS is one of the few models that can work with inaccurate data.

KW - algebraic Bayesian networks

KW - knowledge pattern

KW - logical and probabilistic graphical models

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85093822375&partnerID=8YFLogxK

U2 - 10.1109/SCM50615.2020.9198792

DO - 10.1109/SCM50615.2020.9198792

M3 - Conference contribution

AN - SCOPUS:85093822375

T3 - Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020

SP - 31

EP - 33

BT - Proceedings of 2020 23rd International Conference on Soft Computing and Measurements, SCM 2020

A2 - Shaposhnikov, S.

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 27 May 2020 through 29 May 2020

ER -

ID: 88231016