Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference
We propose a novel framework for performing quan-titative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in case of inconsistent qualitative knowledge. A hi-erarchical Bayesian model is proposed for integrat-ing inconsistent qualitative knowledge by calculat-ing 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 in-ference 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.
Qualitative knowledge modeling In-consistent knowledge integration Bayesian net-works Bayesian inference Monte Carlo simulation
Rui Chang Martin Stetter Wilfried Brauer
Department of Computer Science, Technical University of Munich, Germany Information&Communication, Corporate Technology, Siemens AG, Munich, Germany
国际会议
The 2007 International Conference on Intelligent Systems and Knowledge Engineering(第二届智能系统与知识工程国际会议)
成都
英文
358-365
2007-10-15（万方平台首次上网日期，不代表论文的发表时间）