@inproceedings{8bfb235c2a8f4d79b5709af256e3539a,
title = "Computing concise representations of semi-graphoid independency models",
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 = "S. Lopatatatzidis and {van der Gaag}, L.C.",
year = "2015",
doi = "10.1007/978-3-319-20807-7_26",
language = "English",
isbn = "978-3-319-20806-0",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer",
pages = "290--300",
editor = "S. Destercke and Th. Denoeux",
booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty",
address = "Germany",
}