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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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Yunfei Zhang, Fangqi Zhu, Qiuping Li, Zehang Qiu and Yajun Xie
Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospat...
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Na Lu and Bin Meng
Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and ...
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Moritz Schubert and Dominik Endres
Embodiment of an avatar is important in many seated VR applications. We investigate a Bayesian Causal Inference model of body ownership. According to the model, when available sensory signals (e.g., tactile and visual signals) are attributed to a single ...
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Austin D. Lewis and Katrina M. Groth
Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current sys...
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