Standard

Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks. / Zolotin, Andrey A.; Tulupyev, Alexander L.

Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017. ed. / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2017. p. 22-24 7970483.

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

Harvard

Zolotin, AA & Tulupyev, AL 2017, Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks. in S Shaposhnikov (ed.), Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017., 7970483, Institute of Electrical and Electronics Engineers Inc., pp. 22-24, 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017, St. Petersburg, Russian Federation, 24/05/17. https://doi.org/10.1109/SCM.2017.7970483

APA

Zolotin, A. A., & Tulupyev, A. L. (2017). Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks. In S. Shaposhnikov (Ed.), Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017 (pp. 22-24). [7970483] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCM.2017.7970483

Vancouver

Zolotin AA, Tulupyev AL. Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks. In Shaposhnikov S, editor, Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 22-24. 7970483 https://doi.org/10.1109/SCM.2017.7970483

Author

Zolotin, Andrey A. ; Tulupyev, Alexander L. / Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks. Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017. editor / S. Shaposhnikov. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 22-24

BibTeX

@inproceedings{5bdaa8e694364665a74da9a4a2006376,
title = "Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks",
abstract = "Algorithm of global posteriori inference in algebraic Bayesian networks is considered in the paper. The results obtained earlier for local a posteriori inference are briefly presented. Main steps of global propagation algorithm are described in details. A transition matrix from the vector of knowledge pattern elements to the virtual evidence, propagated to the next knowledge pattern, is proposed. The stated theorem describes the matrix-vector representation of the stochastic evidence propagation algorithm within a network with scalar estimates of the knowledge patterns elements probabilities of truth. The obtained results form the basis for development of the global posteriori inference machine matrix-vector representation in algebraic Bayesian networks and simplify its further software implementation.",
keywords = "algebraic Bayesian networks, global inference, ideal of conjuncts, matrix-vector equations, probabilistic graphical models",
author = "Zolotin, {Andrey A.} and Tulupyev, {Alexander L.}",
year = "2017",
month = jul,
day = "6",
doi = "10.1109/SCM.2017.7970483",
language = "English",
pages = "22--24",
editor = "S. Shaposhnikov",
booktitle = "Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "20th IEEE International Conference on Soft Computing and Measurements, SCM 2017 ; Conference date: 24-05-2017 Through 26-05-2017",

}

RIS

TY - GEN

T1 - Matrix-vector algorithms of global posteriori inference in algebraic Bayesian networks

AU - Zolotin, Andrey A.

AU - Tulupyev, Alexander L.

PY - 2017/7/6

Y1 - 2017/7/6

N2 - Algorithm of global posteriori inference in algebraic Bayesian networks is considered in the paper. The results obtained earlier for local a posteriori inference are briefly presented. Main steps of global propagation algorithm are described in details. A transition matrix from the vector of knowledge pattern elements to the virtual evidence, propagated to the next knowledge pattern, is proposed. The stated theorem describes the matrix-vector representation of the stochastic evidence propagation algorithm within a network with scalar estimates of the knowledge patterns elements probabilities of truth. The obtained results form the basis for development of the global posteriori inference machine matrix-vector representation in algebraic Bayesian networks and simplify its further software implementation.

AB - Algorithm of global posteriori inference in algebraic Bayesian networks is considered in the paper. The results obtained earlier for local a posteriori inference are briefly presented. Main steps of global propagation algorithm are described in details. A transition matrix from the vector of knowledge pattern elements to the virtual evidence, propagated to the next knowledge pattern, is proposed. The stated theorem describes the matrix-vector representation of the stochastic evidence propagation algorithm within a network with scalar estimates of the knowledge patterns elements probabilities of truth. The obtained results form the basis for development of the global posteriori inference machine matrix-vector representation in algebraic Bayesian networks and simplify its further software implementation.

KW - algebraic Bayesian networks

KW - global inference

KW - ideal of conjuncts

KW - matrix-vector equations

KW - probabilistic graphical models

UR - http://www.scopus.com/inward/record.url?scp=85027124475&partnerID=8YFLogxK

U2 - 10.1109/SCM.2017.7970483

DO - 10.1109/SCM.2017.7970483

M3 - Conference contribution

AN - SCOPUS:85027124475

SP - 22

EP - 24

BT - Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017

A2 - Shaposhnikov, S.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017

Y2 - 24 May 2017 through 26 May 2017

ER -

ID: 36985044