Results 281 to 290 of about 118,631 (311)
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Queueing Systems, 1998
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Ganesh, A +3 more
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Ganesh, A +3 more
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2021
Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The
Scutari, Marco, Denis, Jean-Baptiste
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Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The
Scutari, Marco, Denis, Jean-Baptiste
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2013
This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model.
Marcus Bendtsen, José M. Peña 0001
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This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model.
Marcus Bendtsen, José M. Peña 0001
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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
The Bayesian networks can express the joint probabilistic distribution compactly between variables and can express the conditionally independence conveniently. The joint probabilistic influence from the parents to their child can be got from the Bayesian network structure however parents are not necessarily have common influence to their child, which ...
Qingsong Peng +3 more
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The Bayesian networks can express the joint probabilistic distribution compactly between variables and can express the conditionally independence conveniently. The joint probabilistic influence from the parents to their child can be got from the Bayesian network structure however parents are not necessarily have common influence to their child, which ...
Qingsong Peng +3 more
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Inference in Bayesian networks
Nature Biotechnology, 2006Bayesian networks are increasingly important for integrating biological data and for inferring cellular networks and pathways. What are Bayesian networks and how are they used for inference?
Chris J, Needham +3 more
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Parameterising Bayesian Networks
2004Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs) The difficulties involved have led to growing interest in machine learning of BNs from data There is a further need for combining what can be learned from the data with what can be elicited from DEs In this paper, we propose a ...
Owen Woodberry +3 more
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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
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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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