On the role of interaction in sequential Monte Carlo algorithms

Nick Whiteley, Anthony Lee, Kari Heine

Research output: Contribution to journalArticle

22 Citations (Scopus)
17 Downloads (Pure)

Abstract

We introduce a general form of sequential Monte Carlo algorithm defined in terms of a pa- rameterized resampling mechanism. We find that a suitably generalized notion of the Effective Sample Size (ESS), widely used to monitor algorithm degeneracy, appears naturally in a study of its convergence properties. We are then able to phrase sufficient conditions for time-uniform convergence in terms of algorithmic control of the ESS, in turn achievable by adaptively modu- lating the interaction between particles. This leads us to suggest novel algorithms which are, in senses to be made precise, provably stable and yet designed to avoid the degree of interaction which hinders parallelization of standard algorithms. As a byproduct, we prove time-uniform convergence of the popular adaptive resampling particle filter.
Original languageEnglish
Pages (from-to)494-529
Number of pages36
JournalBernoulli
Volume22
Issue number1
Early online date30 Sep 2015
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • convergence
  • hidden
  • particle filters

Fingerprint Dive into the research topics of 'On the role of interaction in sequential Monte Carlo algorithms'. Together they form a unique fingerprint.

  • Cite this