Abstract
Causal Probabilistic Networks (CPN), a method of reasoning using probabilities, has become popular over the last few years within the AI probability and uncertainty community. This paper begins with an introduction to this paradigm, followed by a presentation of some of the current approaches in the induction of the structure learning in CPN. The paper concludes with a concise presentation of alternative approaches to the problem, and the conclusions of this review.
This work was supported by grant from the Gobierno Vasco — Departamento de Educación Universidades e Investigación PGV 92-20
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Larrañaga, P., Yurramendi, Y. (1993). Structure learning approaches in causal probabilistics networks. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028204
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DOI: https://doi.org/10.1007/BFb0028204
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