# Rao-Blackwellized Particle Smoothing as Message Passing

## TL;DR

This paper introduces a novel Rao-Blackwellized particle smoother for conditionally linear Gaussian state-space models, using a factor graph approach to improve fixed-lag smoothing accuracy and efficiency.

## Contribution

It develops a new Rao-Blackwellized particle smoothing method tailored for conditionally linear Gaussian models, enhancing existing smoothing techniques with a factor graph framework.

## Key findings

- Improved smoothing accuracy demonstrated on a specific model.
- Reduced computational complexity compared to traditional methods.
- Effective point mass approximation of the joint smoothing distribution.

## Abstract

In this manuscript the fixed-lag smoothing problem for conditionally linear Gaussian state-space models is investigated from a factor graph perspective. More specifically, after formulating Bayesian smoothing for an arbitrary state-space model as forward-backward message passing over a factor graph, we focus on the above mentioned class of models and derive a novel Rao-Blackwellized particle smoother for it. Then, we show how our technique can be modified to estimate a point mass approximation of the so called joint smoothing distribution. Finally, the estimation accuracy and the computational requirements of our smoothing algorithms are analysed for a specific state-space model.