Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
An approach to sensitivity analysis of inference equations in algebraic bayesian networks. / Zolotin, Andrey A.; Malchevskaya, Ekaterina A.; Tulupyev, Alexander L.; Sirotkin, Alexander V.
Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017. ред. / Sergey Kovalev; Andrey Sukhanov; Margreta Vasileva; Valery Tarassov; Vaclav Snasel; Ajith Abraham. Springer Nature, 2018. стр. 34-42 (Advances in Intelligent Systems and Computing; Том 679).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - An approach to sensitivity analysis of inference equations in algebraic bayesian networks
AU - Zolotin, Andrey A.
AU - Malchevskaya, Ekaterina A.
AU - Tulupyev, Alexander L.
AU - Sirotkin, Alexander V.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - An approach to the sensitivity analysis of local a posterior inference equations in algebraic Bayesian networks is proposed in the paper. Performed a sensitivity analysis of first a posterior inference task for stochastic and deterministic evidences propagated into the knowledge pattern with scalar estimates. For each of the considered cases the necessary metrics are chosen and transformations are carried out, that result into a linear programming problem. In addition, for each type of evidence theorems that postulate upper sensitivity estimates are formulated and proofs are provided. Theoretical results are implemented in CSharp using the module of probabilistic-logical inference software complex. A series of computational experiments is conducted. The results of experiments are visualized using tables and charts. The proposed visualization demonstrates the high sensitivity of the considered models, that confirms the correctness of their use.
AB - An approach to the sensitivity analysis of local a posterior inference equations in algebraic Bayesian networks is proposed in the paper. Performed a sensitivity analysis of first a posterior inference task for stochastic and deterministic evidences propagated into the knowledge pattern with scalar estimates. For each of the considered cases the necessary metrics are chosen and transformations are carried out, that result into a linear programming problem. In addition, for each type of evidence theorems that postulate upper sensitivity estimates are formulated and proofs are provided. Theoretical results are implemented in CSharp using the module of probabilistic-logical inference software complex. A series of computational experiments is conducted. The results of experiments are visualized using tables and charts. The proposed visualization demonstrates the high sensitivity of the considered models, that confirms the correctness of their use.
KW - Algebraic bayesian network
KW - Evidence propagation
KW - Machine learning
KW - Matrix-vector equations
KW - Posterior inference
KW - Probabilistic graphical model
KW - Sensitivity statistical estimate
UR - http://www.scopus.com/inward/record.url?scp=85031429180&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/approach-sensitivity-analysis-inference-equations-algebraic-bayesian-networks
U2 - 10.1007/978-3-319-68321-8_4
DO - 10.1007/978-3-319-68321-8_4
M3 - Conference contribution
AN - SCOPUS:85031429180
SN - 9783319683201
T3 - Advances in Intelligent Systems and Computing
SP - 34
EP - 42
BT - Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017
A2 - Kovalev, Sergey
A2 - Sukhanov, Andrey
A2 - Vasileva, Margreta
A2 - Tarassov, Valery
A2 - Snasel, Vaclav
A2 - Abraham, Ajith
PB - Springer Nature
T2 - 2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017
Y2 - 14 September 2017 through 16 September 2017
ER -
ID: 36984823