Adaptive stopping for fast particle smoothing

@article{Taghavi2013AdaptiveSF,
  title={Adaptive stopping for fast particle smoothing},
  author={Ehsan Taghavi and Fredrik Lindsten and Lennart Svensson and Thomas B. Sch{\"o}n},
  journal={2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
  year={2013},
  pages={6293-6297}
}
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… CONTINUE READING