  We present a framework for online inference in the presence of a
nonexhaustively defined set of classes that incorporates supervised
classification with class discovery and modeling. A Dirichlet process prior
(DPP) model defined over class distributions ensures that both known and
unknown class distributions originate according to a common base distribution.
In an attempt to automatically discover potentially interesting class
formations, the prior model is coupled with a suitably chosen data model, and
sequential Monte Carlo sampling is used to perform online inference. Our
research is driven by a biodetection application, where a new class of pathogen
may suddenly appear, and the rapid increase in the number of samples
originating from this class indicates the onset of an outbreak.
