Path storage in the particle filter
Pierre E. Jacob (National University of Singapore), Lawrence Murray, (CSIRO Mathematics, Informatics & Statistics), Sylvain Rubenthaler (Univ., Nice Sophia Antipolis)

TL;DR
This paper addresses efficient path storage in particle filters, presenting a theoretical memory bound and an algorithm, supported by numerical experiments, to optimize memory use in sequential Monte Carlo methods.
Contribution
It introduces a novel algorithm for storing particle filter paths with a proven memory bound, improving efficiency over existing methods.
Findings
Memory cost bounded by T + C N log N
Algorithm demonstrated with numerical experiments
Efficient path storage in particle filters
Abstract
This article considers the problem of storing the paths generated by a particle filter and more generally by a sequential Monte Carlo algorithm. It provides a theoretical result bounding the expected memory cost by where is the time horizon, is the number of particles and is a constant, as well as an efficient algorithm to realise this. The theoretical result and the algorithm are illustrated with numerical experiments.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
