Results 51 to 60 of about 7,255,480 (346)
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications [PDF]
Journal of Computational Physics, 2020 We propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs ...
Xuhui Meng, H. Babaee, G. Karniadakis
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Modeling dynamic reliability using dynamic Bayesian networks [PDF]
, 2006 This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this
Noyes, Daniel, Tchangani, Ayeley
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Open Mathematics, 2018 The problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to
Wang Jingyun, Liu Sanyang
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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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Reliability Engineering & System Safety, 2020 Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems.
O. Kammouh, P. Gardoni, G. Cimellaro
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Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series [PDF]
, 2011 To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level.
Dondelinger, F. +8 more
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Application of Bayesian networks to generate synthetic health data
J. Am. Medical Informatics Assoc., 2020 OBJECTIVE This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data.
Dhamanpreet Kaur +6 more
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Balanced Quantum-Like Bayesian Networks
Entropy, 2020 Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions.
Andreas Wichert +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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Safety Science, 2019 System safety, reliability and risk analysis are important tasks that are performed throughout the system life-cycle to ensure the dependability of safety-critical systems.
Sohag Kabir, Y. Papadopoulos
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