Standard

Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks. / Tulupyev, A.L.; Sirotkin, A.V.; Zolotin, A.A.

In: Vestnik St. Petersburg University: Mathematics, Vol. 48, No. 3, 2015, p. 168-174.

Research output: Contribution to journal › Article › peer-review

Harvard

Tulupyev, AL, Sirotkin, AV & Zolotin, AA 2015, 'Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks', Vestnik St. Petersburg University: Mathematics, vol. 48, no. 3, pp. 168-174. https://doi.org/10.3103/S1063454115030073

APA

Tulupyev, A. L., Sirotkin, A. V., & Zolotin, A. A. (2015). Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks. Vestnik St. Petersburg University: Mathematics, 48(3), 168-174. https://doi.org/10.3103/S1063454115030073

Vancouver

Tulupyev AL, Sirotkin AV, Zolotin AA. Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks. Vestnik St. Petersburg University: Mathematics. 2015;48(3):168-174. https://doi.org/10.3103/S1063454115030073

Author

Tulupyev, A.L. ; Sirotkin, A.V. ; Zolotin, A.A. / Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks. In: Vestnik St. Petersburg University: Mathematics. 2015 ; Vol. 48, No. 3. pp. 168-174.

BibTeX

@article{0ddd92aa957e4f0d88f2237340b5d35e,
title = "Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks",
abstract = "A posteriori inference is one of three kinds of inference that underlie the processing of knowledge patterns with probabilistic uncertainty in intelligent decision making systems using alge braic Bayesian networks (ABNs). In this paper, the key terms and formulations of theorems describing local a posteriori inference in algebraic Bayesian networks are given in a matrix vector language. The main result is that matrix equations are constructed for normalizing factors appearing in the formulas of a posteriori probabilities of proposition quanta and ideals of conjuncts. The matrix equations of local a priori inference formulated in general not only simplify the preparation of specifications of appropriate inference algorithms and make their implementation more transparent, but also open a possibility for application of classical mathematical techniques to the analysis of the properties of inference results.",
keywords = "probabilistic logic, Bayesian networks, probabilistic logic inference, normalizing factors, uncertain knowledge, evidence propagation, consistency",
author = "A.L. Tulupyev and A.V. Sirotkin and A.A. Zolotin",
year = "2015",
doi = "10.3103/S1063454115030073",
language = "English",
volume = "48",
pages = "168--174",
journal = "Vestnik St. Petersburg University: Mathematics",
issn = "1063-4541",
publisher = "Pleiades Publishing",
number = "3",

}

RIS

TY - JOUR

T1 - Matrix equations for normalizing factors in local a posteriori inference of truth estimates in algebraic Bayesian networks

AU - Tulupyev, A.L.

AU - Sirotkin, A.V.

AU - Zolotin, A.A.

PY - 2015

Y1 - 2015

N2 - A posteriori inference is one of three kinds of inference that underlie the processing of knowledge patterns with probabilistic uncertainty in intelligent decision making systems using alge braic Bayesian networks (ABNs). In this paper, the key terms and formulations of theorems describing local a posteriori inference in algebraic Bayesian networks are given in a matrix vector language. The main result is that matrix equations are constructed for normalizing factors appearing in the formulas of a posteriori probabilities of proposition quanta and ideals of conjuncts. The matrix equations of local a priori inference formulated in general not only simplify the preparation of specifications of appropriate inference algorithms and make their implementation more transparent, but also open a possibility for application of classical mathematical techniques to the analysis of the properties of inference results.

AB - A posteriori inference is one of three kinds of inference that underlie the processing of knowledge patterns with probabilistic uncertainty in intelligent decision making systems using alge braic Bayesian networks (ABNs). In this paper, the key terms and formulations of theorems describing local a posteriori inference in algebraic Bayesian networks are given in a matrix vector language. The main result is that matrix equations are constructed for normalizing factors appearing in the formulas of a posteriori probabilities of proposition quanta and ideals of conjuncts. The matrix equations of local a priori inference formulated in general not only simplify the preparation of specifications of appropriate inference algorithms and make their implementation more transparent, but also open a possibility for application of classical mathematical techniques to the analysis of the properties of inference results.

KW - probabilistic logic

KW - Bayesian networks

KW - probabilistic logic inference

KW - normalizing factors

KW - uncertain knowledge

KW - evidence propagation

KW - consistency

U2 - 10.3103/S1063454115030073

DO - 10.3103/S1063454115030073

M3 - Article

VL - 48

SP - 168

EP - 174

JO - Vestnik St. Petersburg University: Mathematics

JF - Vestnik St. Petersburg University: Mathematics

SN - 1063-4541

IS - 3

ER -

ID: 3969508