Bernoulli Race Particle Filters
Abstract
When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace the true weights with non-negative unbiased estimates. This algorithm is still valid but at the cost of higher variance of the resulting filtering estimates in comparison to a particle filter using the true weights. We propose here a novel algorithm that allows for resampling according to the true intractable weights when only an unbiased estimator of the weights is available. We demonstrate our algorithm on several examples.
Cite
Text
Schmon et al. "Bernoulli Race Particle Filters." Artificial Intelligence and Statistics, 2019.Markdown
[Schmon et al. "Bernoulli Race Particle Filters." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/schmon2019aistats-bernoulli/)BibTeX
@inproceedings{schmon2019aistats-bernoulli,
title = {{Bernoulli Race Particle Filters}},
author = {Schmon, Sebastian M. and Doucet, Arnaud and Deligiannidis, George},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {2350-2358},
volume = {89},
url = {https://mlanthology.org/aistats/2019/schmon2019aistats-bernoulli/}
}