The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
Abstract
It is now over a decade since the pioneering contribution of Gordon (1993), which is commonly regarded as the first instance of modern sequential Monte Carlo (SMC) approaches. Initially focussed on applications to tracking and vision, these techniques are now very widespread and have had a significant impact in virtually all areas of signal and image processing concerned with Bayesian dynamical models. This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,502
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,550
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Stochastic Processes and Filtering Theory
(1970) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(09)x6022-4 · Cited by 7,302
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,759
New Results in Linear Filtering and Prediction Theory
Kalman, R. E., & Bucy, R. S. (1961) · Journal of Basic Engineering · doi:10.1115/1.3658902 · Cited by 6,387
Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
Predicting Taxi–Passenger Demand Using Streaming Data
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2262376 · Cited by 721
Particle filter theory and practice with positioning applications
Gustafsson, F. (2010) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2010.5546308 · Cited by 720
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature
Arasaratnam, I., Haykin, S., & Elliott, R. J. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.894705 · Cited by 622
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 441
Metadata
- Authors
- Cappe, O., Godsill, S. J., & Moulines, E.
- Venue
- Proceedings of the IEEE
- Published
- 2007-05
- Volume
- 95
- Issue
- 5
- Pages
- 899-924
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2055936398
- Cited by
- 1,009
- References
- 102
- FWCI
- 55.55
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo. https://app.cochat.ai/papers/10.1109/jproc.2007.893250
- The paper
- Cappe, O., Godsill, S. J., & Moulines, E. (2007). An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2007.893250
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
