Abstract
This paper proposes a new approach to the problem of ob- taining the most probable explanations given a set of observations in a Bayesian network. The method provides a set of possibilities ordered by their probabilities. The main novelties are that the level of detail of each one of the explanations is not uniform (with the idea of being as simple as possible in each case), the explanations are mutually exclusive, and the number of required explanations is not fixed (it depends on the particular case we are solving). Our goals are achieved by means of the construction of the so called explanation tree which can have asym- metric branching and that will determine the different possibilities. This paper describes the procedure for its computation based on information theoretic criteria and shows its behaviour in some simple examples.
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Flores, M.J., Gámez, J.A., Moral, S. (2005). Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_7
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DOI: https://doi.org/10.1007/11518655_7
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