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Estimating online the parameters of general state-space hidden Markov modelsis a topic of importance in many scientific and engineering disciplines. Inthis paper we present an online parameter estimation algorithm obtained bycasting our recently proposed particle-based, rapid incremental smoother(PaRIS) into the framework of recursive maximum likelihood estimation forgeneral hidden Markov models. Previous such particle implementations sufferfrom either quadratic complexity in the number of particles or from thewell-known degeneracy of the genealogical particle paths. By using thecomputational efficient and numerically stable PaRIS algorithm for estimatingthe needed prediction filter derivatives we obtain a fast algorithm with acomputational complexity that grows only linearly with the number of particles.The efficiency and stability of the proposed algorithm are illustrated in asimulation study.

Efficient parameter inference in general hidden Markov models using the filter derivatives