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.
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Acknowledgments
The authors would like to thank Peter de Waal for verifying the main results of the reported research and the referees for their helpful comments.
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Lopatatzidis, S., van der Gaag, L.C. (2015). Computing Concise Representations of Semi-graphoid Independency Models. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_26
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DOI: https://doi.org/10.1007/978-3-319-20807-7_26
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