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- W2467778979 abstract "The conditional independencies from a joint probability distribution constitute a model which is closed under the semi-graphoid properties of independency. These models typically are exponentially large in size and cannot be feasibly enumerated. For describing a semi-graphoid model therefore, researchers have proposed a more concise representation. This representation is composed of a representative subset of the independencies involved, called a basis, and lets all other independencies be implicitly defined by the semi-graphoid properties. An algorithm is available for computing such a basis for a semi-graphoid independency model. In this paper, we identify some new properties of a basis in general which can be exploited for arriving at an even more concise representation of a semi-graphoid model. Based upon these properties, we present an enhanced algorithm for basis construction which never returns a larger basis for a given independency model than currently existing algorithms. • Necessary conditions for excluding given independencies from basis computation. • Properties of an independency relation that help reduce the size of a representative basis. • An algorithm for basis computation that improves on earlier ones in terms of result and efficiency." @default.
- W2467778979 created "2016-07-22" @default.
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- W2467778979 date "2017-01-01" @default.
- W2467778979 modified "2023-09-27" @default.
- W2467778979 title "Concise representations and construction algorithms for semi-graphoid independency models" @default.
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- W2467778979 doi "https://doi.org/10.1016/j.ijar.2016.06.011" @default.
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