Lookahead Strategies for Sequential Monte Carlo

TL;DR

This paper explores lookahead strategies in sequential Monte Carlo methods, providing theoretical insights and new algorithms to improve inference by utilizing future information efficiently.

Contribution

It introduces novel lookahead techniques for SMC that enhance inference accuracy without significantly increasing computational costs.

Findings

Theoretical justification for new lookahead algorithms

Improved SMC performance with future information utilization

Efficient algorithms balancing accuracy and computational load

Abstract

Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.