Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
Signal Processing
2018-11-21 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1811.05933,
title = {Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems},
author = {Arash Mehrjou and Bernhard Schölkopf},
journal= {arXiv preprint arXiv:1811.05933},
year = {2018}
}