Efficient and Flexible Inference for Stochastic Systems

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Stefan Bauer, Nico S. Gorbach, Djordje Miladinovic, Joachim M. Buhmann


Many real world dynamical systems are described by stochastic differential equations. Thus parameter inference is a challenging and important problem in many disciplines. We provide a grid free and flexible algorithm offering parameter and state inference for stochastic systems and compare our approch based on variational approximations to state of the art methods showing significant advantages both in runtime and accuracy.