GeNIeRate: An interactive generator of diagnostic Bayesian network models

Authors:

Pieter Kraaijeveld
Man Machine Interaction Group Delft University of Technology Mekelweg 4, 2628 CD Delft, the Netherlands e-mail: p.c.kraaijeveld@ewi.tudelft.nl

Marek J. Druzdzel
University of Pittsburgh
Department of Information Science
e-mail: marek@sis.pitt.edu

Abstract:

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 such 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 modelled 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.