Results 51 to 60 of about 207,453 (329)
Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference.
Yang Xiang, Hanwen Zheng
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Bayesian dynamic financial networks with time-varying predictors
, 2014 We propose a Bayesian nonparametric model including time-varying predictors in dynamic network inference. The model is applied to infer the dependence structure among financial markets during the global financial crisis, estimating effects of verbal and ...
Dunson, David B., Durante, Daniele
core +1 more source
Bayesian inference for dynamic social network data [PDF]
Journal of Statistical Planning and Inference, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Koskinen, Johan H., Snijders, Tom A B
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R
Journal of Statistical SoftwareDynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation.
David Quesada +2 more
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Reliability and Service Life Analysis of Airbag Systems
Mathematics, 2023 Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model.
Hongyan Dui, Jiaying Song, Yun-an Zhang
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Optimal Population Coding for Dynamic Input by Nonequilibrium Networks
Entropy, 2022 The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However,
Kevin S. Chen
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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
Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network
Applied SciencesBrake systems are subjected to various factors such as wear and fatigue over a long period of time. They bring a great challenge to the reliability analysis of the braking system.
Yingjie Tian, Jing Wen, Shubin Zheng
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