Security Based Multiple Bayesian Models Combination Approach
Decision making in medical domain often involves incorporating new evidences into existing or working models reflecting the decision problems at hand. The authors propose a new framework that facilitates effective aggregation of multiple Bayesian Network models. The proposed framework aims to minimize time and effort required to customize and extend the original models through preserving the conditional independence relationships inherent in two or more types of Bayesian network models. They present an algorithm to systematically combine the qualitative and the quantitative parts of the different Bayesian models. Combination of Bayesian models involves integrating both structural and parameters of different models. They also describe how effective the presented algorithm and it can reduce total computational complexity.