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B1077
Title: Parallelising particle filters with butterfly interactions Authors:  Kari Heine - University of Bath (United Kingdom) [presenting]
Nick Whiteley - University of Bristol (United Kingdom)
Ali Taylan Cemgil - Bogazigi University (Turkey)
Abstract: 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 BPF arises from particle interactions that typically make parallel implementations of BPF nontrivial. We propose a method whereby the 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 suggest that the constraints on the stagewise interactions introduce error 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.