Bernoulli Race Particle Filters


Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis ;
Proceedings of Machine Learning Research, PMLR 89:2350-2358, 2019.


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.

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