Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Arash Mehrjou; Bernhard Schölkopf

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arXiv · 1811.05933

Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems

Arash Mehrjou·Bernhard Schölkopf

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Abstract

Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussianity and Affinity. We propose a model to relax both these assumptions based on recent advances in implicit generative models. Empirical results show that the proposed method gives a significant advantage over GF and nonlinear methods based on fixed nonlinear kernels.

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BibTeX RIS

Arash Mehrjou, Bernhard Schölkopf. 2018-11-14. Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems. https://arxiv.org/abs/1811.05933

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