An efficient particle-based online EM algorithm for general state-space models
2015 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 48, no 28, 963-968 p.Article in journal (Refereed) Published
Estimating the parameters of general state-space models is a topic of importance for many scientific and engineering disciplines. In this paper we present an online parameter estimation algorithm obtained by casting our recently proposed particle-based, rapid incremental smoother (PaRIS) into the framework of online expectation-maximization (EM) for state-space models proposed by Cappé (2011). Previous such particle-based implementations of online EM suffer typically from either the well-known degeneracy of the genealogical particle paths or a quadratic complexity in the number of particles. However, by using the computationally efficient and numerically stable PaRIS algorithm for estimating smoothed expectations of timeaveraged sufficient statistics of the model we obtain a fast algorithm with very limited memory requirements and a computational complexity that grows only linearly with the number of particles. The efficiency of the algorithm is illustrated in a simulation study.
Place, publisher, year, edition, pages
2015. Vol. 48, no 28, 963-968 p.
EM algorithm, parameter estimation, particle filters, recursive estimation, state space models
IdentifiersURN: urn:nbn:se:kth:diva-195392DOI: 10.1016/j.ifacol.2015.12.255ScopusID: 2-s2.0-84988598857OAI: oai:DiVA.org:kth-195392DiVA: diva2:1048849
QC 201611222016-11-222016-11-032016-11-22Bibliographically approved