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The Markov modulated (switching) state space is an important model paradigmin applied statistics. In this article, we specifically consider Markovmodulated nonlinear state-space models and address the online Bayesianinference problem for such models. In particular, we propose a newRao-Blackwellized particle filter for the inference task which is our maincontribution here. The detailed descriptions including an algorithmic summaryare subsequently presented.

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