PQPN: A New Qualitative Abstraction of Bayesian Network

Abstract

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.