Abstract
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.
The paper presents results of the project partially supported with RFBR grant 15- 01-09001-a “Combined Probabilistic-Logic Graphical Approach to Representation and Processing of Uncertain Knowledge Systems: Algebraical Bayesian Networks and Related Models”.
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References
Afenyo, M., Khan, F., Veitch, B., Yang, M.: Arctic shipping accident scenario analysis using Bayesian network approach. Ocean Eng. 133, 224–230 (2017)
Dalir, F., Motlagh, M., Ashrafi, K.: Sensitivity analysis of parameters affecting carbon footprint of fossil fuel power plants based on life cycle assessment scenarios. Global J. Environ. Sci. Manage. 3(1), 75–88 (2017)
Depaoli, S., Yang, Y., Felt, J.: Using bayesian statistics to model uncertainty in mixture models: a sensitivity analysis of priors. Struct. Equ. Model. Multidisciplinary J. 24(2), 198–215 (2017)
Nejati, H., Moosavi, S.: A new brittleness index for estimation of rock fracture toughness. J. Mining Environ. 8(1), 83–91 (2017)
Romaniello, V., Piscini, A., Bignami, C., Anniballe, R., Stramondo, S.: A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3D range data. J. Appl. Remote Sens. 11(1) (2017)
Tulupyev, A.L., Nikolenko, S.I., Sirotkin, A.V.: Bayesian networks: a probabilistic-logic approach. SPb.: Nauka (2006). (in Russian)
Tulupyev, A.L., Sirotkin, A.V., Nikolenko, S.I.: Bayesian belief networks. SPb.: SPbSU Press (2009). (in Russian)
Tulupyev, A.L., Sirotkin, A.V., Zolotin, A.A.: Matrix equations in a posteriori inference of truth estimates in algebraic Bayesian networks. Vestnik St. Petersburg Univ. Math. 48(3), 168–174 (2015)
Xing, F.W., Chen, J.L., Zhao, B.L., Jiang, J.Z., Tang, A.L., Chen, Y.L.: Real role of beta-blockers in regression of left ventricular mass in hypertension patients Bayesian network meta-analysis. Medicine 96(10), e6290 (2017)
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Zolotin, A.A., Malchevskaya, E.A., Tulupyev, A.L., Sirotkin, A.V. (2018). An Approach to Sensitivity Analysis of Inference Equations in Algebraic Bayesian Networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_4
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DOI: https://doi.org/10.1007/978-3-319-68321-8_4
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