Belief updating and learning in semi qualitative probabilistic networks myfox bumper dating

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.

We present an enhanced formalism of qualitative probabilistic networks to provide for a finer level of representation detail.

An enhanced qualitative probabilistic network differs from a basic qualitative network in that it distinguishes between strong and weak influences.

This work was supported by the National Natural Science Foundation of China (No.

Qualitative probabilistic networks can be seen as qualitative abstractions of probabilistic, or Bayesian (belief), networks: a directed acyclic graph is used to model probabilistic (in)dependence, but instead of providing conditional probabilities to encode a joint probability distribution, only constraints on conditional probability distributions are required.

Qualitative probabilistic networks were first introduced by Mike Wellman as qualitative abstractions of Influence Diagrams in Fundamental Concepts of Qualitative Probabilistic Networks. Max Henrion and Marek Druzdzel introduced an additional type of qualitative relationship: Wellman and Henrion studied the relation between the two types of synergy and the relation between product synergy and a special kind of intercausal reasoning: explaining away.

Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. (2008) Recovering the Global Structure from Multiple Local Bayesian Networks.

As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. International Journal on Artificial Intelligence Tools, 17, 1067-1088.

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