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Belief updating and learning in semi qualitative probabilistic networks

Belief updating and learning in semi qualitative probabilistic networks


One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences. Qualitative networks abstract from the numerical probabilities of their quantitative counterparts by using signs to summarise the probabilistic influences between their variables. Note that [20] suggest that their transformations could be applied in the ordinal case, but using principles that [3] restricted to the numerical interpretation. In the ordinal interpretation, some methods have been proposed to estimate a possibility distribution from infinitesimal probabilities [23, 24, 25]. The experiments in this paper investigate this relation when this assumption is not satisfied. Previous article in issue. We show that, at least for the example examined, the ordering of faults coincide as long as all the causal relations in the original probabilistic network are taken into account. Specifically, we abstract a probabilistic belief network for diagnosing faults into a kappa network and compare the ordering of faults computed using both methods. Enhanced qualitative networks are purely qualitative in nature, as basic qualitative networks are, yet allow for resolving some trade-offs upon inference. But little has been done on utilizing their connection as outlined above. The reported results have important implications on the use of kappa rankings to enhance the knowledge engineering of uncertainty models.

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Belief updating and learning in semi qualitative probabilistic networks. Computer Science > Artificial Intelligence.

Belief updating and learning in semi qualitative probabilistic networks


One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences. Qualitative networks abstract from the numerical probabilities of their quantitative counterparts by using signs to summarise the probabilistic influences between their variables. Note that [20] suggest that their transformations could be applied in the ordinal case, but using principles that [3] restricted to the numerical interpretation. In the ordinal interpretation, some methods have been proposed to estimate a possibility distribution from infinitesimal probabilities [23, 24, 25]. The experiments in this paper investigate this relation when this assumption is not satisfied. Previous article in issue. We show that, at least for the example examined, the ordering of faults coincide as long as all the causal relations in the original probabilistic network are taken into account. Specifically, we abstract a probabilistic belief network for diagnosing faults into a kappa network and compare the ordering of faults computed using both methods. Enhanced qualitative networks are purely qualitative in nature, as basic qualitative networks are, yet allow for resolving some trade-offs upon inference. But little has been done on utilizing their connection as outlined above. The reported results have important implications on the use of kappa rankings to enhance the knowledge engineering of uncertainty models.

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4 thoughts on “Belief updating and learning in semi qualitative probabilistic networks

  1. [RANDKEYWORD
    Zulkirg

    We also provide a formal analysis of some network structures where the two methods will differ.

  2. [RANDKEYWORD
    Malaktilar

    One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences.

  3. [RANDKEYWORD
    Gogar

    We also provide a formal analysis of some network structures where the two methods will differ.

  4. [RANDKEYWORD
    Bazuru

    We show that, at least for the example examined, the ordering of faults coincide as long as all the causal relations in the original probabilistic network are taken into account. One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences.

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