Results 61 to 70 of about 235,639 (329)
, 2011 Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence.
Cheng, J., Druzdzel, M. J.
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Advanced Functional Materials, EarlyView.We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu +3 more
wiley +1 more source
Probabilistic Prognosis with Dynamic Bayesian Networks
International Journal of Prognostics and Health Management, 2015 This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats
Gregory Bartram, Sankaran Mahadevan
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Prediction of fatigue crack propagation based on dynamic Bayesian network
Advances in Mechanical Engineering, 2022 To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper.
Wei Wang +4 more
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Bayesian anomaly detection methods for social networks
, 2010 Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still.
Hand, David J. +3 more
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Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity
Advanced Functional Materials, EarlyView.Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen +11 more
wiley +1 more source
Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks [PDF]
, 2018 The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented for the problem of tracking a target dynamic model using a time-varying network of heterogeneous sensing agents.
Bandyopadhyay, Saptarshi, Chung, Soon-Jo
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Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
International Journal of Distributed Sensor Networks, 2016 This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model.
Pao-Kuan Wu, Tsung-Chih Hsiao, Ming Xiao
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Asynchronous Dynamic Bayesian Networks
, 2012 Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves.
Pfeffer, Avi, Tai, Terry
openaire +2 more sources