Bernoulli Race Particle Filters
This addresses a bottleneck in particle filtering for applications with intractable weights, offering a potential improvement in estimation accuracy, though it appears incremental as it builds on existing unbiased estimation approaches.
The paper tackles the problem of particle filter resampling when true weights are intractable by proposing a novel algorithm that enables resampling with true weights using only unbiased estimates, resulting in reduced variance compared to state-of-the-art methods that rely on estimated weights.
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.