  We introduce a new sequential Monte Carlo algorithm we call the particle
cascade. The particle cascade is an asynchronous, anytime alternative to
traditional particle filtering algorithms. It uses no barrier synchronizations
which leads to improved particle throughput and memory efficiency. It is an
anytime algorithm in the sense that it can be run forever to emit an unbounded
number of particles while keeping within a fixed memory budget. We prove that
the particle cascade is an unbiased marginal likelihood estimator which means
that it can be straightforwardly plugged into existing pseudomarginal methods.
