Relaxing the Local Independence Assumption for QuantitativeLearning in Acyclic Directed Graphical Models through HierarchicalPartition

نویسندگان

  • Daniela Golinelli
  • David Madigan
  • Guido Consonni
چکیده

The simplest method proposed by Spiegelhal-ter and Lauritzen (1990) to perform quantitative learning in ADG presents a potential weakness: the local independence assumption. We propose to alleviate this problem through the use of Hierarchical Partition Models. Our approach is compared with the previous one from an interpretative and pre-dictive point of view.

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تاریخ انتشار 1999