Parallelizing particle filters with butterfly interactions

Kari Heine, Nick Whiteley and A.Taylan Cemgil

Scandinavian Journal of Statistics, 2020, vol. 47, issue 2, 361-396

Abstract: The bootstrap particle filter (BPF) is the cornerstone of many algorithms used for solving generally intractable inference problems with hidden Markov models. The long‐term stability of the BPF arises from particle interactions that typically make parallel implementations of the BPF nontrivial. We propose a method whereby particle interaction is done in several stages. With the proposed method, full interaction can be accomplished even if we allow only pairwise communications between processing elements at each stage. We show that our method preserves the consistency and the long‐term stability of the BPF, although our analysis suggests that the constraints on the stagewise interactions introduce errors leading to a lower convergence rate than standard Monte Carlo. The proposed method also suggests a new, more flexible, adaptive resampling scheme, which, according to our numerical experiments, is the method of choice, displaying a notable gain in efficiency in certain parallel computing scenarios.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/sjos.12408

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:47:y:2020:i:2:p:361-396

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898

Access Statistics for this article

Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist

More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().