When: Friday,March 18, 2005, 1:00 p.m.

Where: Room 501, IS Building

Who: Pieter Kraaijeveld and Mark Voortman

First talk will be,
"GeNIeRate: An Interactive Generator of Diagnostic Bayesian Network Models"- by Pieter Kraaijeveld.

Abstract: Constructing diagnostic Bayesian network models is a complex and time consuming task. In this talk, the speaker will talk about 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. The talk will also feature a short demo of GeNIeRate.

followed by:
"Improving and learning Bayesian networks from cases"- by Mark Voortman.

Abstract: This talk will be divided into two parts. The first part will be about the integration of cases into GeNIe. GeNIe is Bayesian networks modeling software developed by the Decision Systems Laboratory, SIS, University of Pittsburgh. There will be a short demo. The second part will be more theoretical and is about improving and learning Bayesian networks from cases.

Speakers Bios: Pieter Kraaijeveld is a student from the Delft University of Technology (The Netherlands) working on his master's thesis at the Decision Systems Laboratory of the School of Information Sciences, University of Pittsburgh. His current research interest are in the area of modeling large Diagnostic Bayesian networks.

Mark Voortman is a Masters student working on his master's thesis at the Decision Systems Laboratory of the School of Information Sciences, University of Pittsburgh. He is from Delft University of Technology (The Netherlands). His current research interests are in the areas of artificial intelligence, especially learning Bayesian networks.