Online sequential Monte Carlo EM algorithm

Online (or recursive) estimation of fixed model parameters in general state-space models is a crucial but often difficult task. This paper is about likelihood-based point estimation, showing that an online EM (Expectation-Maximization) algorithm recently proposed for discrete hidden Markov models can be extended to more general settings, including non-linear non-Gaussian state-space models that necessitate the use of sequential Monte Carlo filtering approximations. The performance of the proposed online sequential Monte Carlo EM algorithm is illustrated on numerical examples.