Results 231 to 240 of about 399,768 (265)

Bayesian networks

Neurocomputing, 2010
What are Bayesian networks and why are their applications growing across all fields?
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Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017
Physics based simulations of manufacturing processes are used for prediction of material properties and defects in a number of industrial applications. However, a practising engineer often requires the solution to an "inverse problem" - prediction of inputs for the desired outcome.
Avadhut Sardeshmukh, Sreedhar Reddy, Gautham B. P., Amol Joshi   +3 more
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Bayesian network management

Queueing Systems, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ganesh, A, O'Connell, N, Pitts, S, Green, PJ   +3 more
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Bayesian Networks

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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Gated Bayesian Networks

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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Parameterising Bayesian Networks

2004
Most 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, Ann E. Nicholson, Kevin B. Korb, Carmel A. Pollino   +3 more
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Variant Bayesian Networks

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, Ming Zhang, Weimin Wu 0001, Ronggui Wang   +3 more
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Inference in Bayesian networks

Nature Biotechnology, 2006
Bayesian 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, James R, Bradford, Andrew J, Bulpitt, David R, Westhead   +3 more
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Qualitative Bayesian networks

Information Sciences, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bayesian Assessment of Network Reliability

SIAM Review, 1998
Summary: 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, Nozer D. Singpurwalla, Adrian Smith   +2 more
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