A time series is a vector of events collected over time. Contrary to standard random samples, the observations in a time series are dependent. I develop automated methods for modeling and clustering time series. This research is supported by NSF.

Clustering: I developed BCD, a Bayesian model-based algorithm for clustering categorical time series on the basis of their dynamics. BCD was extended to clustering continuous time series and was applied to profiling gene expression data.

Incremental Bayesian segmentation: The idea is to develop methods for processing a time series on line and for incrementally recognizing changes in the underlying dynamics.




Copyright © 2001 by Paola Sebastiani
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