Classification framework for partially observed dynamical systems
Physical Review E
American Physical Society
©2017 American Physical Society
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
This work was supported by the EPSRC grant “Personalised Medicine Through Learning in the Model Space” (grant number EP/L000296/1). KT-A gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1.
This is the author accepted manuscript. The final version is freely available from American Physical Society via the DOI in this record.
Vol. 95, Iss. 4, 043303