Online inference in Markov modulated nonlinear dynamic systems: a Rao-Blackwellized particle filtering approach
Computation
2013-11-27 v1
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.
Keywords
Cite
@article{arxiv.1311.6486,
title = {Online inference in Markov modulated nonlinear dynamic systems: a Rao-Blackwellized particle filtering approach},
author = {Saikat Saha and Gustaf Hendeby},
journal= {arXiv preprint arXiv:1311.6486},
year = {2013}
}