  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.
