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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, November 28, 2000}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Guenther Walther}
\centerline{\sl Stanford University}
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\centerline{\bf Bikernel Oscillation Analysis for the Mixture Complexity}
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The problem under consideration is to determine the number of components in
a mixture, in the case where one does not want to make parametric
assumptions on the components. Some results are given as to when such an
approach is feasible, and when it is not. From these results a simple but
powerful criterion is derived. The problem can then be analyzed by a
statistical technique which has recently transcended from computer vision
into statistics. I will compare the method with other approaches and
illustrate its use with some examples, also addressing the case where
parametric models are more appropriate.

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