Asynchronous Anytime Sequential Monte Carlo

TL;DR

The paper presents the particle cascade, an asynchronous, anytime sequential Monte Carlo algorithm that improves efficiency and memory usage while providing unbiased marginal likelihood estimates, suitable for integration into existing methods.

Contribution

It introduces the particle cascade, a novel asynchronous, anytime particle filtering algorithm that eliminates barrier synchronizations and maintains unbiased estimates.

Findings

Improves particle throughput and memory efficiency.

Provides unbiased marginal likelihood estimates.

Compatible with existing pseudomarginal methods.

Abstract

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.