Key points are not available for this paper at this time.
Abstract A new algorithm for the prediction, filtering, and smoothing of non-Gaussian nonlinear state space models is shown. The algorithm is based on a Monte Carlo method in which successive prediction, filtering (and subsequently smoothing), conditional probability density functions are approximated by many of their realizations. The particular contribution of this algorithm is that it can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low. Several numerical examples are shown.
Building similarity graph...
Analyzing shared references across papers
Loading...
Genshiro Kitagawa (Fri,) studied this question.
www.synapsesocial.com/papers/6a0ee57a2eca052da647e50b — DOI: https://doi.org/10.1080/10618600.1996.10474692
Genshiro Kitagawa
Journal of Computational and Graphical Statistics
The Institute of Statistical Mathematics
Building similarity graph...
Analyzing shared references across papers
Loading...