Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference
- 10.2991/iske.2007.55How to use a DOI?
- Qualitative Knowledge Modeling, Inconsistent Knowledge Integration, Bayesian Networks, Bayesian Inference, Monte Carlo Simulation
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.
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Rui Chang AU - Wilfried Brauer PY - 2007/10 DA - 2007/10 TI - Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 326 EP - 333 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.55 DO - 10.2991/iske.2007.55 ID - Chang2007/10 ER -