LAUSR

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 point estimates of model parameters to represent individual data items, we employ posterior distributions over model parameters, 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 test beds: a biological pathway model and a stochastic double-well system. Crucially, we show that the classification performance is not impaired when the model structure used for inferring posterior distributions is much more simple than the observation-generating model structure, provided the reduced-complexity inferential model structure captures the essential characteristics needed for the given classification task.