Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2018
- DOI:
- 10.48550/arXiv.1811.05933
- arXiv:
- arXiv:1811.05933
- Bibcode:
- 2018arXiv181105933M
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Machine Learning;
- Statistics - Machine Learning