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

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.