Bernoulli Race Particle Filters

Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2350-2358, 2019.

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 this Paper

BibTeX
@InProceedings{pmlr-v89-schmon19a, title = {Bernoulli Race Particle Filters}, author = {Schmon, Sebastian M. and Doucet, Arnaud and Deligiannidis, George}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {2350--2358}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/schmon19a/schmon19a.pdf}, url = {https://proceedings.mlr.press/v89/schmon19a.html}, 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.} }
Endnote
%0 Conference Paper %T Bernoulli Race Particle Filters %A Sebastian M. Schmon %A Arnaud Doucet %A George Deligiannidis %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-schmon19a %I PMLR %P 2350--2358 %U https://proceedings.mlr.press/v89/schmon19a.html %V 89 %X 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.
APA
Schmon, S.M., Doucet, A. & Deligiannidis, G.. (2019). Bernoulli Race Particle Filters. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2350-2358 Available from https://proceedings.mlr.press/v89/schmon19a.html.

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