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About 10,000 papers for “Bayesian network”

Bayesian network

Muhammad Al-Btoush · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

Bayesian network

Muhammad Al-Btoush · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

Bayesian Network

Afizan Azman, Qinggang Meng, Eran A. Edirisinghe et al. · 2011

Abstract Driver distractions can be categorized into 3 major parts: visual, cognitive and manual. Visual and manual distraction on a driver can be physically detected. However, assessing cognitive distraction is difficult since it is more of an “internal ” distraction rather than any easily measured “external ” distraction. There are several methods available that can be used to detect driver cognitive. Physiological measurements, performance measurements (primary and secondary tasks) and rating scales are some of the well-known measurements usually used to detect cognitive distraction. This study focused on physiological measurements, specifically on a driver’s eye and mouth movements. Six different participants were involved in our experiment. The duration of the experiment was 8 minutes and 49 seconds for each participant. Eye and mouth movements were obtained using the FaceLAB Seeing Machine cameras and their magnitudes of the r-values were found more than 60% thus proving that they are strongly correlated to each other. 1.

Cited by 0

Bayesian Network

Jisu Hu · Figshare · 2015

Bayesian Network

Cited by 0Open access

Bayesian network

本村 陽一 · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · 2006

Cited by 0Open access

Bayesian network

María Julia, Flores Gallego, Departamento De Informática et al. · 2001

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Bayesian Network

Ahmed Rebai · RePEc: Research Papers in Economics

Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century.

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ALARM Bayesian network.

Raúl Vicente Casaña-Eslava (9073230), Sandra Ortega-Martorell (126305), Paulo J. Lisboa (9073233) et al. · Figshare · 2020

Cited by 0Open access

Consensus Bayesian network .

Liangdong Hu (283207), Limin Wang (87511) · Figshare · 2015

<p>27 genes were identified from the EcoCyc ROS detoxification pathway, while genes , , , were identified by the prediction program.</p>

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Bayesian network structure.

Nicholas L. Rider (10150845), Gina Cahill (10150848), Tina Motazedi (10150851) et al. · Figshare · 2021

<p><b>A</b>. An example Bayesian network with parent and child nodes connected by arcs. <b>B</b>. The general structure of our network components. Training data (*may be updated over time) and patient features allow for improving inference (i.e. probabilities) and structure over time making these networks dynamic. (EHR = Electronic Health Record).</p>

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Causal Bayesian network.

Yan Zhang (8098), Hye Kyong Kweon (427451), Christian Shively (427452) et al. · Figshare · 2015

<p>Each edge indicates a potential causal influence between proteins, which might be a direct or indirect influence. It does not distinguish activation and inhibition. The thicker the edge, the higher the posterior probability.</p>

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