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Adaptive stopping for fast particle smoothing

Particle smoothing is useful for offline state inference and parameter learning in nonlinear/non-Gaussian state-space models. However, many particle smoothers, such as the popular forward filter/backward simulator (FFBS), are plagued by a quadratic computational complexity in the number of particles. One approach to tackle this issue is to use rejection-sampling-based FFBS (RS-FFBS), which asymptotically reaches linear complexity. In practice, however, the constants can be quite large and the actual gain in computational time limited. In this contribution, we develop a hybrid method, governed by an adaptive stopping rule, in order to exploit the benefits, but avoid the drawbacks, of RS-FFBS. The resulting particle smoother is shown in a simulation study to be considerably more computationally efficient than both FFBS and RS-FFBS. / CNDM / CADICS

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-93461
Date January 2013
CreatorsTaghavi, Ehsan, Lindsten, Fredrik, Svensson, Lennart, Schön, Thomas B.
PublisherLinköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan, Linköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan, School of Computational Science and Engineering, McMaster University, Division of Signals and Systems, Chalmers University
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeConference paper, info:eu-repo/semantics/conferenceObject, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationProceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), p. 6293-6297

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