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Adaptive Stopping for Fast Particle Smoothing

Ehsan Taghavi (Institutionen för signaler och system) ; F. Lindsten ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling) ; T. B. Schon
2013 Ieee International Conference on Acoustics, Speech and Signal Processing (1520-6149). p. 6293-6297. (2013)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Sequential Monte Carlo, particle smoothing, backward simulation, CHAIN MONTE-CARLO

Denna post skapades 2014-02-27. Senast ändrad 2016-07-21.
CPL Pubid: 194267


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