Online inference in Markov modulated nonlinear dynamic systems: a Rao-Blackwellized particle filtering approach

TL;DR

This paper introduces a novel Rao-Blackwellized particle filter for efficient online Bayesian inference in Markov modulated nonlinear state-space models, enhancing real-time analysis capabilities.

Contribution

The paper presents a new Rao-Blackwellized particle filtering method specifically designed for Markov modulated nonlinear state-space models, advancing inference techniques.

Findings

Improved online inference accuracy

Enhanced computational efficiency

Applicability to complex dynamic systems

Abstract

The Markov modulated (switching) state space is an important model paradigm in applied statistics. In this article, we specifically consider Markov modulated nonlinear state-space models and address the online Bayesian inference problem for such models. In particular, we propose a new Rao-Blackwellized particle filter for the inference task which is our main contribution here. The detailed descriptions including an algorithmic summary are subsequently presented.