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Computing concise representations of semi-graphoid independency models

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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, 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.
Keywords
ALGORITHMS, CONDITIONAL-INDEPENDENCE

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Chicago
Lopatatzidis, Stavros, and Linda van der Gaag. 2015. “Computing Concise Representations of Semi-graphoid Independency Models.” In Lecture Notes in Artificial Intelligence, 9161:290–300.
APA
Lopatatzidis, S., & van der Gaag, L. (2015). Computing concise representations of semi-graphoid independency models. Lecture Notes in Artificial Intelligence (Vol. 9161, pp. 290–300). Presented at the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU).
Vancouver
1.
Lopatatzidis S, van der Gaag L. Computing concise representations of semi-graphoid independency models. Lecture Notes in Artificial Intelligence. 2015. p. 290–300.
MLA
Lopatatzidis, Stavros, and Linda van der Gaag. “Computing Concise Representations of Semi-graphoid Independency Models.” Lecture Notes in Artificial Intelligence. Vol. 9161. 2015. 290–300. Print.
@inproceedings{6839436,
  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, 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.},
  author       = {Lopatatzidis, Stavros and van der Gaag, Linda},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  isbn         = {978-3-319-20807-7},
  issn         = {0302-9743},
  keywords     = {ALGORITHMS,CONDITIONAL-INDEPENDENCE},
  language     = {eng},
  location     = {Compiègne, France},
  pages        = {290--300},
  title        = {Computing concise representations of semi-graphoid independency models},
  url          = {http://dx.doi.org/10.1007/978-3-319-20807-7_26},
  volume       = {9161},
  year         = {2015},
}

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