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R. C. Nichol (Dept. of Physics, Carnegie Mellon), A. J. Connolly (Dept. of Physics & Astronomy, Univ. of Pittsburgh), C. R. Genovese (Dept. of Statistics, Carnegie Mellon), A. W. Moore, J. Schneider (Dept. of Computer Science, Carnegie Mellon), L. Wasserman (Dept. of Statistics, Carnegie Mellon)
We will present in this talk the application of new statistical analyses to large, multi-dimensional databases. This work is part of a new initiative at Carnegie Mellon/Univ. of Pittsburgh which is a multi-disciplinary collaboration between astrophysicists, statisticians and computer scientists. We will discuss specific examples we are working on using the Sloan Digital Sky Survey data which include clustering analyses (how to optimally represent the large scale structure in the universe), spectral classification (choosing the best basis for these astronomical data), fast n-point correlation functions and the use of Bayes Nets to better determine a photometric redshift. These techniques can be extended to other data sources and could be used as part of any ``toolkit'' for the National Virtual Observatory.