IDAGs: A perfect map for any distribution

  • Remco R. Bouckaert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 747)


The notion of relevance that is very important in knowledge based systems can be efficiently encoded by conditional independence. Although directed acyclic graphs (DAG) are powerful means for representing conditional independencies in probability distributions it is not always possible to find a DAG that represents all conditional independencies and dependencies of a distribution. We present a new formalism that is able to do this for positive probability distributions. The main issue is to augment a DAG with a special kind of arcs that induce independencies. Furthermore, an efficient algorithm is presented for building these extended DAGs.


Direct Acyclic Graph Conditional Independence Independency Statement Independency Model Conditional Probability Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Remco R. Bouckaert
    • 1
  1. 1.Department of Computer ScienceUtrecht UniversityTB UtrechtThe Netherlands

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