Vorträge

Approximate Bayesian reasoning

Bert Kappen
University of Nijmegen,
the Netherlands

During the last few years, the use of probabilistic methods in artificial intelligence and machine learning has gained enormous popularity. In particular, probabilistic graphical models have become the preferred method for knowledge representation and reasoning. I will illustrate the application of such models to modeling temporal data such as browsing behaviour on the internet.

However, the drawback of the probabilistic approach is that the method is intractable. This means that the typical computation scales exponentially with the problem size, which prevents large scale applications.

A popular approximation scheme is provided by belief propagation. This method is closely related to the so-called Bethe approximation from statistical physics. In this talk I will show how this method can be applied to probabilistic reasoning, in particular to two applications: genetic linkage analysis and expert systems for medical diagnosis.

Zeit: Donnerstag, den 17. Juni 2004, 17 Uhr c.t.

Ort: Seminarraum Karp - Raum 68,
        Neubau Informatik, Haus 64,
        Erdgeschoß