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Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
Taylor R. Brown · 2020-06-28 · via stat.ML updates on arXiv.org

This paper introduces the {\it particle swarm filter} (not to be confused with particle swarm optimization): 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.

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