Hierarchical Inconsistent Qualitative Knowledge Integration for Quantitative Bayesian Inference

DOI

10.2991/iske.2007.55How to use a DOI?

Keywords

Qualitative Knowledge Modeling, Inconsistent Knowledge Integration, Bayesian Networks, Bayesian Inference, Monte Carlo Simulation

Abstract

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.

Copyright

© 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/).

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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  -