On the role of interaction in sequential Monte Carlo algorithms

Nick Whiteley, Anthony Lee, Kari Heine

Research output: Contribution to journalArticlepeer-review

32 Citations (SciVal)
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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
Issue number1
Early online date30 Sept 2015
Publication statusPublished - 1 Jan 2016


  • convergence
  • hidden
  • particle filters


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