  There is an increasing number of large, digital, synoptic sky surveys, in
which repeated observations are obtained over large areas of the sky in
multiple epochs. Likewise, there is a growth in the number of (often automated
or robotic) follow-up facilities with varied capabilities in terms of
instruments, depth, cadence, wavelengths, etc., most of which are geared toward
some specific astrophysical phenomenon. As the number of detected transient
events grows, an automated, probabilistic classification of the detected
variables and transients becomes increasingly important, so that an optimal use
can be made of follow-up facilities, without unnecessary duplication of effort.
We describe a methodology now under development for a prototype event
classification system; it involves Bayesian and Machine Learning classifiers,
automated incorporation of feedback from follow-up observations, and
discriminated or directed follow-up requests. This type of methodology may be
essential for the massive synoptic sky surveys in the future.
