GeNIeRate: An Interactive Generator of Diagnostic Bayesian Network Models
Pieter Kraaijeveld · 2005
Constructing diagnostic Bayesian network models is a complex and time consuming task. In this paper, we propose a methodology to simplify and speed up the design of very large models. The models are based on two simplifying assumptions: (1) the structure of the model has three levels of variables and (2) the interaction among the variables can be modeled by Noisy-MAX gates. The methodology is implemented in an application named: GeNIeRate, which aims at supporting construction of diagnostic Bayesian network models consisting of hundreds or even thousands of variables. Preliminary qualitative evaluation of this application shows great promise. We are planning to conduct a systematic study to compare GeNIeRate to traditional techniques for building Bayesian network models and we hope to be able to present the results at the workshop. 1