Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference
Rui R. Chang, Wilfried Brauer · 2007
We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge.Here, we focus on the treatment in case of inconsistent qualitative knowledge.A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features.Each inconsistent knowledge component uniquely defines a model class in the hyperspace.A set of constraints within each class is generated to describe the uncertainty in ground Bayesian model space.Quantitative Bayesian inference is approximated by model averaging with Monte Carlo methods.Our method is tested on ASIA network and results suggest that it enables reasonable quantitative Bayesian inference from a set of inconsistent qualitative knowledge.