An intuitive dashboard for Bayesian Network inference
Paper
Paper/Presentation Title | An intuitive dashboard for Bayesian Network inference |
---|---|
Presentation Type | Paper |
Authors | Reddy, Vikas (Author), Farr, Anna Charisse (Author), Wu, Paul (Author), Mengersen, Kerrie (Author) and Yarlagadda, Prasad K. D. V. (Author) |
Journal or Proceedings Title | Journal of Physics: Conference Series |
Journal Citation | 490 (1 - 012023), pp. 1-4 |
Number of Pages | 4 |
Year | 2014 |
Publisher | IOP Publishing |
Place of Publication | Bristol, United Kingdom |
ISSN | 1742-6588 |
1742-6596 | |
Digital Object Identifier (DOI) | https://doi.org/10.1088/1742-6596/490/1/012023 |
Conference/Event | 2nd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE 2013) |
Event Details | 2nd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE 2013) Event Date 01 to end of 05 Sep 2013 Event Location Prague, Czech Republic |
Abstract | 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 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++. |
Keywords | Baynesian networks; Baynesian Network inference; end users; C++ (programming language); application programs; integrated circuits; software packages |
ANZSRC Field of Research 2020 | 339999. Other built environment and design not elsewhere classified |
490599. Statistics not elsewhere classified | |
Public Notes |
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. |
Byline Affiliations | Queensland University of Technology |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5263/an-intuitive-dashboard-for-bayesian-network-inference
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