Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters

06/27/2020

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by   Taylor R. Brown, et al.

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This paper introduces the particle swarm algorithm, a recursive and embarrassingly parallel algorithm that targets an approximation to the sequence of posterior predictive distributions by averaging expectation approximations from many particle filters. A law of large numbers and a central limit theorem are provided, as well as an numerical study of simulated data from a stochastic volatility model.