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Conference Paper: Construction of probabilistic boolean network for credit default data

TitleConstruction of probabilistic boolean network for credit default data
Authors
KeywordsBoolean networks
Probabilistic boolean networks
Inverse problem
Transition probability matrix
Issue Date2014
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002829
Citation
The 7th International Joint Conference on Computational Sciences and Optimization (CSO 2014), Beijing, China, 4-6 July 2014. In Conference Proceedings, 2014, p. 11--15 How to Cite?
AbstractIn this article, we consider the problem of construction of Probabilistic Boolean Networks (PBNs). Previous works have shown that Boolean Networks (BNs) and PBNs have many potential applications in modeling genetic regulatory networks and credit default data. A PBN can be considered as a Markov chain process and the construction of a PBN is an inverse problem. Given the transition probability matrix of the PBN, we try to find a set of BNs with probabilities constituting the given PBN. We propose a revised estimation method based on entropy approach to estimate the model parameters. Practical real credit default data are employed to demonstrate our proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/207211
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLiang, R-
dc.contributor.authorQiu, Y-
dc.contributor.authorChing, WK-
dc.date.accessioned2014-12-19T04:05:09Z-
dc.date.available2014-12-19T04:05:09Z-
dc.date.issued2014-
dc.identifier.citationThe 7th International Joint Conference on Computational Sciences and Optimization (CSO 2014), Beijing, China, 4-6 July 2014. In Conference Proceedings, 2014, p. 11--15-
dc.identifier.isbn978-1-4799-5372-1-
dc.identifier.urihttp://hdl.handle.net/10722/207211-
dc.description.abstractIn this article, we consider the problem of construction of Probabilistic Boolean Networks (PBNs). Previous works have shown that Boolean Networks (BNs) and PBNs have many potential applications in modeling genetic regulatory networks and credit default data. A PBN can be considered as a Markov chain process and the construction of a PBN is an inverse problem. Given the transition probability matrix of the PBN, we try to find a set of BNs with probabilities constituting the given PBN. We propose a revised estimation method based on entropy approach to estimate the model parameters. Practical real credit default data are employed to demonstrate our proposed method.-
dc.languageeng-
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1002829-
dc.relation.ispartofInternational Joint Conference on Computational Sciences and Optimization (CSO)-
dc.rightsInternational Joint Conference on Computational Sciences and Optimization (CSO). Copyright © IEEE Computer Society.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBoolean networks-
dc.subjectProbabilistic boolean networks-
dc.subjectInverse problem-
dc.subjectTransition probability matrix-
dc.titleConstruction of probabilistic boolean network for credit default dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailChing, WK: wching@hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CSO.2014.11-
dc.identifier.hkuros241888-
dc.identifier.spage11-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 141219-

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