An intuitive dashboard for Bayesian network inference
Data(s) |
11/03/2014
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Resumo |
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++. |
Formato |
application/pdf |
Identificador | |
Publicador |
Institute of Physics Publishing Ltd. |
Relação |
http://eprints.qut.edu.au/63346/1/An_Intuitive_Dashboard_for_Bayesian_Network_Inference.pdf http://iopscience.iop.org/1742-6596/490/1/012023 DOI:10.1088/1742-6596/490/1/012023 Reddy, Vikas, Farr, Anna Charisse, Wu, Paul P., Mengersen, Kerrie, & Yarlagadda, Prasad K.D.V. (2014) An intuitive dashboard for Bayesian network inference. In Journal of Physics: Conference Series, Institute of Physics Publishing Ltd., 012023. http://purl.org/au-research/grants/ARC/LP0990135 |
Direitos |
Copyright 2013 [please consult the author] Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd. |
Fonte |
Institute for Future Environments; Science & Engineering Faculty |
Palavras-Chave | #010401 Applied Statistics #010404 Probability Theory #080309 Software Engineering #Bayesian Networks #Inference #Visualisation |
Tipo |
Conference Paper |