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On sequential Monte Carlo sampling methods for Bayesian filtering
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Citations
Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Self-citationSequential 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
Self-citationHe, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
A survey of convergence results on particle filtering methods for practitioners
Crisan, D., & Doucet, A. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.984773 · Cited by 919
Visual tracking and recognition using appearance-adaptive models in particle filters
Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.836152 · Cited by 668
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410
Pezzulo, G., Rigoli, F., & Chersi, F. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00092 · Cited by 163
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
RUIZ, J. J., PULIDO, M., & MIYOSHI, T. (2013) · Journal of the Meteorological Society of Japan. Ser. II · doi:10.2151/jmsj.2013-201 · Cited by 145
Models and Algorithms for Tracking of Maneuvering Objects Using Variable Rate Particle Filters
Godsill, S. J., Vermaak, J., Ng, W., & Li, J. F. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.894708 · Cited by 81
Self-citationMbalawata, I. S., Särkkä, S., & Haario, H. (2013) · Computational Statistics · doi:10.1007/s00180-012-0352-y · Cited by 77
Divide et impera : subgoaling reduces the complexity of probabilistic inference and problem solving
Maisto, D., Donnarumma, F., & Pezzulo, G. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.1335 · Cited by 59
Unsupervised detection and tracking of moving objects for video surveillance applications
Elafi, I., Jedra, M., & Zahid, N. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.08.008 · Cited by 37
Cadini, F., Lomazzi, L., Ferrater Roca, M., Sbarufatti, C., & Giglio, M. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108048 · Cited by 25
Metadata
- Authors
- Doucet, A., Godsill, S., & Andrieu, C.
- Venue
- Statistics and Computing
- Published
- 2000-07
- Volume
- 10
- Issue
- 3
- Pages
- 197-208
- ISSN
- 0960-3174 (ISSN-L)
- 1573-1375
- OpenAlex
- W2126736494
- Cited by
- 4,411
- References
- 0
- FWCI
- 55.53
- Type
- journal article
- Language
- en
- Open access
- closed
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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.