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Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl?s causality, and determ...
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Danilo Pau, Andrea Pisani and Antonio Candelieri
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger ...
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Xiaotong Sun, Yu Xin, Zuocai Wang, Minggui Yuan and Huan Chen
This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the ...
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Wenyun Tang, Jiahui Chen, Chao Sun, Hanbing Wang and Gen Li
Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regardi...
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Yasser T. Matbouli and Suliman M. Alghamdi
A holistic occupational and economy-wide framework for salary prediction is developed and tested using statistical machine learning (ML). Predictive models are developed based on occupational features and organizational characteristics. Five different su...
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Jared A. Cook, Ralph C. Smith, Jason M. Hite, Razvan Stefanescu and John Mattingly
Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous de...
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