Variational Resampling

Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren

Abstract:

We cast the resampling step in particle filters
(PFs) as a variational inference problem, resulting in a new class of resampling schemes:
variational resampling. Variational resampling is flexible as it allows for choices of 1)
divergence to minimize, 2) target distribution
to input to the divergence, and 3) divergence
minimization algorithm. With this novel application of VI to particle filters, variational
resampling further unifies these two powerful and popular methodologies. We construct
two variational resamplers that replicate particles in order to maximize lower bounds
with respect to two different target measures.
We benchmark our variational resamplers on
challenging smoothing tasks, outperforming
PFs that implement the state-of-the-art resampling schemes.