Asynchronous Anytime Sequential Monte Carlo
This addresses computational bottlenecks in sequential Monte Carlo methods for researchers in probabilistic modeling, though it is incremental as it builds on existing particle filtering techniques.
The paper tackles the inefficiency of traditional particle filters by introducing the particle cascade, an asynchronous, anytime algorithm that improves particle throughput and memory efficiency, and proves it is an unbiased marginal likelihood estimator.
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.