Belief updating and learning in semi qualitative probabilistic networks


05-Jun-2019 08:58

Preliminary experiments verify the correctness and feasibility of our methods.This work was supported by the National Natural Science Foundation of China (No.The first one, using constraint-based algorithms, is based on the probabilistic semantic of Bayesian networks.Links are added or deleted according to the results of statistical tests, which identify marginal and conditional independencies.In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold.In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences.The second approach, using score-based algorithms, is based on a metric that measures the quality of candidate networks with respect to the observed data.This metric trades off network complexity against the degree of fit to the data, which is typically expressed as the likelihood of the data given the network.

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We first show that exact inferences with SQPNs are NPPP-Complete., are typically estimated with the maximum likelihood approach from the observed frequencies in the dataset associated with the network.



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