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Bootstrap particle filter (BPF) is the corner stone of many popularalgorithms used for solving inference problems involving time series that areobserved through noisy measurements in a non-linear and non-Gaussian context.The long term stability of BPF arises from particle interactions which in thecontext of modern parallel computing systems typically means that particleinformation needs to be communicated between processing elements, which makesparallel implementation of BPF nontrivial.  In this paper we show that it is possible to constrain the interactions in away which, under some assumptions, enables the reduction of the cost ofcommunicating the particle information while still preserving the consistencyand the long term stability of the BPF. Numerical experiments demonstrate thatalthough the imposed constraints introduce additional error, the proposedmethod shows potential to be the method of choice in certain settings.

Parallelising Particle Filters with Butterfly Interactions