Results 21 to 30 of about 124,846 (268)
Research on Human Factors Reliability of Electric Power Enterprises Based on HFCRA Model [PDF]
SHS Web of Conferences, 2023 Based on Bayesian network, this paper establishes a human factor analysis tool-Human Factor Classification and Reliability Analysis (HFCRA) model, and analyzes the key factors of human error in power enterprises by combining the human events and data of ...
Liu Junfeng +3 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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Future Internet, 2023 For the current stage of complex and changing network environments and correlated and synchronized vulnerability attacks, this study first fuses attack graph technology and Bayesian networks and constructs Bayesian attack graphs toportray the correlation
Kongpei Wu, Huiqin Qu, Conggui Huang
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Testing Bayesian Networks [PDF]
IEEE Transactions on Information Theory, 2020 This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node.
Clément L. Canonne +3 more
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The Scientific World Journal, 2014 A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was ...
Jiali Wang, Qingnian Zhang, Wenfeng Ji
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Discrete Dynamics in Nature and Society, 2014 This paper presents a Bayesian network model for estimating origin-destination matrices. Most existing Bayesian methods adopt prior OD matrixes, which are always troublesome to be obtained.
Lin Cheng +3 more
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CoRR, 2023 This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution.
Alex Graves +3 more
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Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion [PDF]
International Journal of Electronics and Telecommunications, 2021 In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multisource information fusion.
Xin Zeng +2 more
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CoRR, 2018 This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Vikram Mullachery +2 more
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Performance optimization scheme of turboshaft aeroengine based on Bayesian network
Xibei Gongye Daxue Xuebao, 2021 The turboshaft aeroengine is mainly used in helicopters. As a power device that drives the rotor to generate lift and propulsion, it has been rapidly developed in recent years. The manufacturing process of turboshaft aeroengine is complex, and there is a
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