Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3508
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dc.contributor.authorLuo, P-
dc.contributor.authorZhan, G-
dc.contributor.authorHe, Q-
dc.contributor.authorShi, Z-
dc.contributor.authorLü, K-
dc.coverage.spatial7en
dc.date.accessioned2009-07-22T10:56:03Z-
dc.date.available2009-07-22T10:56:03Z-
dc.date.issued2007-
dc.identifier.citationTransactions on Information Theory, 53 (9):3233-3239en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/3508-
dc.description.abstractPartition entropy is the numerical metric of uncertainty within a partition of a finite set, while conditional entropy measures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies, are actually additional constraints on its entropy. Thus, in this paper partition entropy is defined as a function of probability distribution, satisfying all the inequalities of not only partition entropy itself but also its conditional counterpart. These inequality postulates formalize the intuitive understandings of uncertainty contained in partitions of finite sets.We study the relationships between these inequalities, and reduce the redundancies among them. According to two different definitions of conditional entropy from its partition entropy, the convenient and unified checking conditions for any partition entropy are presented, respectively. These properties generalize and illuminate the common nature of all partition entropies.en
dc.format.extent260690 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.subjectTerms—Conditional entropy, inequality, partition entropy, uncertainty.en
dc.titleOn defining partition entropy by inequalitiesen
dc.typeResearch Paperen
Appears in Collections:Business and Management
Brunel Business School Research Papers

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