  We present a general framework for classifying partially observed dynamical
systems based on the idea of learning in the model space. In contrast to the
existing approaches using model point estimates to represent individual data
items, we employ posterior distributions over models, thus taking into account
in a principled manner the uncertainty due to both the generative
(observational and/or dynamic noise) and observation (sampling in time)
processes. We evaluate the framework on two testbeds - a biological pathway
model and a stochastic double-well system. Crucially, we show that the
classifier performance is not impaired when the model class used for inferring
posterior distributions is much more simple than the observation-generating
model class, provided the reduced complexity inferential model class captures
the essential characteristics needed for the given classification task.
