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Measures of variability for Bayesian network graphical structures

Abstract:

The structure of a Bayesian network includes a great deal of information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study its variability, which can be used to compare the performance of different learning algorithms and to measure the strength of any arbitrary subset of arcs. In this paper we will introduce some descriptive statistics and the corresponding parametric and ...

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Publication status:
Not published
Peer review status:
Not peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
arXiv.org
Journal:
arXiv.org More from this journal
Publication date:
2011-12-06
Keywords:
Pubs id:
pubs:724266
UUID:
uuid:212c84a7-f2f7-4035-9174-6f3f82101d9d
Local pid:
pubs:724266
Source identifiers:
724266
Deposit date:
2017-08-25

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