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Bayesian Networks
The Experts below are selected from a list of 73776 Experts worldwide ranked by ideXlab platform
Alexandre Evsukoff – 1st expert on this subject based on the ideXlab platform
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Bayesian Networks inference algorithm to implement Dempster Shafer theory in reliability analysis
Reliability Engineering and System Safety, 2008Co-Authors: C Simon, P. Weber, Alexandre EvsukoffAbstract:This paper deals with the use of Bayesian Networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian Networks are identified. The basic concepts of the Bayesian Networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian Networks‘ inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis. © 2007 Elsevier Ltd. All rights reserved.
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Bayesian Networks inference algorithm to implement Dempster Shafer theory in reliability analysis
Reliability Engineering and System Safety, 2008Co-Authors: Christophe Simon, Philippe Weber, Alexandre EvsukoffAbstract:This paper deals with the use of Bayesian Networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian Networks are identified. The basic concepts of the Bayesian Networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian Networks‘ inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.
Avi Pfeffer – 2nd expert on this subject based on the ideXlab platform
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object oriented Bayesian Networks
Uncertainty in Artificial Intelligence, 1997Co-Authors: Daphne Koller, Avi PfefferAbstract:Bayesian Networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian Networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies, Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN–particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts—can also be used to speed up the inference process.
Daphne Koller – 3rd expert on this subject based on the ideXlab platform
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object oriented Bayesian Networks
Uncertainty in Artificial Intelligence, 1997Co-Authors: Daphne Koller, Avi PfefferAbstract:Bayesian Networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian Networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies, Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN–particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts—can also be used to speed up the inference process.