Results 321 to 330 of about 7,255,480 (346)
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Bayesian Assessment of Network Reliability
SIAM Review, 1998Summary: The recent technological advances in communications, manufacturing, and transportation systems have made networks the mainstay of modern life. Consequently, the reliability of networks has become an important issue and much progress has been made in its assessment. However, the state of the art here suffers from a serious limitation.
Nicholas Lynn +2 more
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Information Sciences, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Applications of Bayesian Networks
SSRN Electronic Journal, 2012Modelling cause and effect relationships has been a major challenge for statisticians in a wide range of application areas. Bayesian Networks (BN) combine graphical analysis with Bayesian analysis to represent causality maps linking measured and target variables. Such maps can be used for diagnostics and predictive analytics.
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BAYESIAN NETWORKS IN EDUCATIONAL TESTING
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004In this paper we discuss applications of Bayesian networks to educational testing. Namely, we deal with the diagnosis of person's skills. We show that when modeling dependence between skills we can get better diagnosis faster. We present results of experiments with basic operations that use fractions.
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Sensitivity analysis in Bayesian networks
1995For systems based on Bayesian networks, evidence is used to compute posterior probabilities for some hypotheses. Sensitivity analysis is concerned with questions on how sensitive the conclusion is to the evidence provided. After the basic definitions and an example we conclude that the heart of sensitivity analysis is to compute probabilities for the ...
Jensen, F. V. +2 more
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Bayesian Networks and Decision Graphs
Statistics for Engineering and Information Science, 2001F. Jensen
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2002
We present a bayesian framework for XML document retrieval. This framework allows us to consider content only and content and structure queries. We perform the retrieval task using inference in our network. Our model can adapt to a specific corpora through parameter learning.
Piwowarski, Benjamin +2 more
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We present a bayesian framework for XML document retrieval. This framework allows us to consider content only and content and structure queries. We perform the retrieval task using inference in our network. Our model can adapt to a specific corpora through parameter learning.
Piwowarski, Benjamin +2 more
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Bayesian learning for neural networks: an algorithmic survey
Artificial Intelligence Review, 2023Alexandros Iosifidis +1 more
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Structural learning of mixed noisy-OR Bayesian networks
International Journal of Approximate Reasoning, 2023Jiří Vomlel +2 more
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