Adaptive Stopping for Fast Particle Smoothing
Paper i proceeding, 2013

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.

backward simulation

CHAIN MONTE-CARLO

Sequential Monte Carlo

particle smoothing

Författare

Ehsan Taghavi

Chalmers, Signaler och system

F. Lindsten

Linköpings universitet

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

T. B. Schon

Linköpings universitet

2013 Ieee International Conference on Acoustics, Speech and Signal Processing

1520-6149 (ISSN)

6293-6297

Ämneskategorier

Elektroteknik och elektronik

DOI

10.1109/ICASSP.2013.6638876

ISBN

978-1-4799-0356-6