Deep nonlinear non-gaussian filtering for dynamical systems
A Mehrjou, B Schölkopf - arXiv preprint arXiv:1811.05933, 2018 - arxiv.org
arXiv preprint arXiv:1811.05933, 2018•arxiv.org
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 …
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 …
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.
arxiv.org