Bernoulli race particle filters

Conference item

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.

Authors: Schmon, S; Deligiannidis, G; Doucet, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: MLR Press
• Host title: Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics
• Journal: 22nd International Conference on Artificial Intelligence and Statistics (AIStats 2019)
• Volume: 89
• Pages: 2350-2358
• Publication date: 2019-04-11
• Acceptance date: 2018-12-22
• Keywords: unbiased estimation; Rao-Blackwellization; Bernoulli factory; random weight particle filter