Sequential Importance Sampling With Corrections For Partially Observed States

TL;DR

This paper introduces a sequential importance sampling method with correction steps for systems with partially observed states, enabling better inference in dynamic Bayesian models like invasive species spread.

Contribution

It proposes a novel iterative correction approach within sequential importance sampling to handle incompatible observations over time.

Findings

Effective correction criteria identified for maintaining importance weights.

Method successfully applied to invasive species monitoring scenarios.

Improves inference accuracy with partially observed, evolving systems.

Abstract

We consider an evolving system for which a sequence of observations is being made, with each observation revealing additional information about current and past states of the system. We suppose each observation is made without error, but does not fully determine the state of the system at the time it is made. Our motivating example is drawn from invasive species biology, where it is common to know the precise location of invasive organisms that have been detected by a surveillance program, but at any time during the program there are invaders that have not been detected. We propose a sequential importance sampling strategy to infer the state of the invasion under a Bayesian model of such a system. The strategy involves simulating multiple alternative states consistent with current knowledge of the system, as revealed by the observations. However, a difficult problem that arises is that observations made at a later time are invariably incompatible with previously simulated states. To solve this problem, we propose a two-step iterative process in which states of the system are alternately simulated in accordance with past observations, then corrected in light of new observations. We identify criteria under which such corrections can be made while maintaining appropriate importance weights.