2012/03/12 by Mohamed Osama Ahmed, Ahmed, Mohamed Osama, Pouyan T. Bibalan +5
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Computation (stat.CO) #Data Stream Mining Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #cs.LG #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.1203.2394
16 pages, 11 figures, Authorship in alphabetical order
arxiv created 2012/03/12 · openalex publication_date 2012/03/12 · arxiv updated 2012/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackwellized particle filter (RBPF), where the second step is carried out using Monte Carlo approximations instead of analytical inference. As a result, the range of applications of the DPF is broader than the one for the RBPF. In this paper, we improve the DPF in two ways. First, we derive a Monte Carlo approximation of the optimal proposal distribution and, consequently, design and implement a more efficient look-ahead DPF. Although the decentralized filters were initially designed to capitalize on parallel implementation, we show that the look-ahead DPF can outperform the standard particle filter even on a single machine. Second, we propose the use of bandit algorithms to automatically configure the state space decomposition of the DPF.