TY - GEN UR - http://lib.ugent.be/catalog/pug01:6839436 ID - pug01:6839436 LA - eng TI - Computing concise representations of semi-graphoid independency models PY - 2015 SN - 978-3-319-20807-7 SN - 0302-9743 PB - 2015 AU - Lopatatzidis, Stavros 000120902921 802001341666 AU - van der Gaag, Linda AB - 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, a more concise representation is used, which is composed of a representative subset of the independencies involved, called a basis, and letting all other independencies be implicitly defined by the semi-graphoid properties; for computing such a basis, an appropriate algorithm is available. Based upon new properties of semi-graphoid models in general, we introduce an improved algorithm that constructs a smaller basis for a given independency model than currently existing algorithms. ER -Download RIS file
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020 | a 978-3-319-20807-7 | ||
022 | a 0302-9743 | ||
024 | a 000364847800026 2 wos | ||
024 | a 1854/LU-6839436 2 handle | ||
024 | a 10.1007/978-3-319-20807-7_26 2 doi | ||
040 | a UGent | ||
245 | a Computing concise representations of semi-graphoid independency models | ||
260 | c 2015 | ||
520 | a 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, a more concise representation is used, which is composed of a representative subset of the independencies involved, called a basis, and letting all other independencies be implicitly defined by the semi-graphoid properties; for computing such a basis, an appropriate algorithm is available. Based upon new properties of semi-graphoid models in general, we introduce an improved algorithm that constructs a smaller basis for a given independency model than currently existing algorithms. | ||
598 | a P1 | ||
100 | a Lopatatzidis, Stavros 0 000120902921 0 802001341666 0 975263288001 | ||
700 | a van der Gaag, Linda | ||
650 | a Technology and Engineering | ||
653 | a ALGORITHMS | ||
653 | a CONDITIONAL-INDEPENDENCE | ||
773 | t 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) g Lecture Notes in Artificial Intelligence. 2015. 9161 p.290-300 q 9161:<290 | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/6839436/file/6839447 z [ugent] y typeinst.pdf | ||
920 | a confcontrib | ||
852 | x EA b TW06 | ||
922 | a UGENT-EA |
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