Particle filter efficiency under limited communication[Distributed stochastic gradient MCMC]

SummarySequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles. The -sequential Monte Carlo method was proposed recently as a potential solution to this that limits communication between particles. This limited communication is controlled through a sequence of stochastic matrices known asmatrices. We study the influence of the communication structure on the convergence and stability properties of the resulting algorithms. In particular, we quantitatively show that the mixing properties of thematrices play an important role in the stability properties of the algorithm. Moreover, we prove that one can ensure good mixing properties by using randomized communication structures where each particle only communicates with a few neighbouring particles. The resulting algorithms converge at the usual Monte Carlo rate. This leads to efficient versions of distributed sequential Monte Carlo.