Qualitative Reasoning of Bayesian Belief Using Meta-Knowledge

Abstract

Ordering composite hypotheses in a Bayesian network based on its associated a posteriori probabilities can be exponentially hard. This paper discusses a qualitative reasoning approach which reduces the computational complexity of deriving a partial ordering of composite hypotheses. Such a reasoning makes use of the meta-knowledge about the causal relationships among individual hypotheses to deduce qualitative conclusions about the ordering of local composite hypotheses. By doing so, we can employ divide and conquer strategy to derive the global ordering of the composite hypotheses from a set of local ordering in which consistencies are guaranteed. We view the contribution of this research is on the integration of qualitative reasoning with the use of local computations to find not only the most likely composite hypotheses, but also the partial ordering of the composite hypotheses.