The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
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.
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Citations
Cites
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Cited by
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Kalman Filtering and Neural Networks
(2001) · Wiley eBooks · doi:10.1002/0471221546 · Cited by 2,386
Independent Component Analysis: Principles and Practice
(2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511624148 · Cited by 1,917
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Sequential Monte Carlo Samplers
Del Moral, P., Doucet, A., & Jasra, A. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2006.00553.x · Cited by 1,239
Metadata
- Authors
- Kitagawa, G.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 1996-03
- Volume
- 5
- Issue
- 1
- Pages
- 1-25
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W2077611006
- Cited by
- 2,404
- References
- 1
- FWCI
- 14.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kitagawa, G. (1996). Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.1996.10474692
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
