PQPN: A New Qualitative Abstraction of Bayesian Network

Qualitative probabilistic networks (QPNs) are a qualitative abstraction of Bayesian networks, which focus on the monotonic relationship between variables. However, sometimes we don´t care about this monotonic relationship, but more concerned about the change of probability of the variable comparing with its prior probability. In this paper, we propose a new qualitative abstraction of Bayesian networks-PQPNs (prior qualitative probabilistic networks) that focuses on the prior probability distribution. We analyze the properties of PQPN, namely symmetry, transitivity and composition. Further, we design a sign propagation algorithm of PQPN, and describe the relationship between QPN and PQPN. Finally, through an experiment, we show that PQPNs have an advantage over QPNs.