Abstract
Once causal networks have been chosen as the model of knowledge representation of our interest, the aim of this work is to assess the performance of polytrees or Singly connected Causal Networks (SCNs) as approximations of general Multiply connected Causal Networks (MCNs). To do that we have carried out a simulation experiment in which we generated a number of MCNs, simulated them to get samples and used these samples to learn the SCNs that approximated the original MCNs, reporting the results.
This work has been supported by the DGICYT under Project PB92-0939
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Acid, S., de Campos, L.M. (1995). Approximations of causal networks by polytrees: an empirical study. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035946
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DOI: https://doi.org/10.1007/BFb0035946
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