Adaptive Variational Particle Filtering in Non-stationary Environments
Machine Learning
2018-07-23 v1 Machine Learning
Abstract
Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-stationary environment by making connections with the online mirror descent algorithm which is known to be a universal online convex optimization algorithm. As a result of this connection, our proposed particle filtering algorithm proves to achieve optimal particle efficiency.
Cite
@article{arxiv.1807.07612,
title = {Adaptive Variational Particle Filtering in Non-stationary Environments},
author = {Mahdi Azarafrooz},
journal= {arXiv preprint arXiv:1807.07612},
year = {2018}
}