Sequential Monte Carlo Methods Papers
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Please, add a link to your paper here.
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2002
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- Nando de Freitas. Rao-Blackwellised
Particle Filtering for Fault Diagnosis. IEEE Aerospace,
2002. Available in
PDF
PS
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2001
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Sequential Monte Carlo Methods in Practice.
Arnaud Doucet - Nando de Freitas - Neil Gordon (eds).
Springer-Verlag, 2001, ISBN 0-387-95146-6.
- Christophe Andrieu, Nando de Freitas, Arnaud Doucet. Rao-Blackwellised
Particle Filtering via Data Augmentation. Advances in Neural Information Processing Systems (NIPS13),
2001.
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Cemgil, A.T. and Kappen, B.
Rhythm Quantization and Tempo Tracking by Sequential
Monte Carlo.
Advances in NIPS, 2001.
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Yukito Iba.
Population Monte Carlo algorithms.
Transactions of the Japanese Society for Artificial
Intelligence, vol.16, no.2, pp.279-286, 2001.
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F. LeGland and N. Oudjane.
Stability and uniform approximation of nonlinear filters using the Hilbert
metric, and application to particle filters.
Research report RR-4215, INRIA.
June, 2001. Abstract.
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Dirk Ormoneit, Christiane Lemieux and David Fleet.
Lattice Particle Filters.
UAI, 2001.
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Matthew Orton and William Fitzgerald.
A Bayesian Approach to Tracking Multiple Targets using Sensor Arrays and Particle Filters.
Technical report, Cambridge University Engineering Department, CUED/F-INFENG/TR.403, 2001.
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Matthew Orton and Alan Marrs.
Incorporation of Out-of-Sequence Measurements in
Non-Linear Dynamic Systems using Particle Filters.
Technical report, 2001.
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Yong Rui and Yunqiang Chen.
Better Proposal Distributions: Object
Tracking Using Unscented Particle Filter.
Proc. of IEEE CVPR 2001, pp. II-786 to 793, Kauai, Hawaii, December 11-13, 2001.
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2000
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Bui, H. H., Venkatesh, S., and West, G.
On the recognition of abstract Markov policies.
Seventeenth National Conference on Artificial Intelligence (AAAI-2000).
Austin, Texas, to appear 2000.
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Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell.
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.
Uncertainty in Artificial Intelligence (UAI2000).
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A. Doucet, S. J. Godsill, and C. Andrieu.
On
sequential Monte Carlo sampling methods for Bayesian filtering.
Statist. Comp., 10:197-208, 2000.
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A. Doucet, S. J. Godsill, and C. Andrieu.
Monte
Carlo filtering and smoothing with application to time-varying spectral
estimation.
In Proc. IEEE International Conference on Acoustics, Speech and Signal
Processing, vol. II, pp. 701-704, 2000.
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Pedro Højen-Sørensen, Nando de Freitas and Torben Fog.
On-Line Probabilistic Classification with Particle Filters.
IEEE International
Workshop on Neural Networks for Signal Processing (NNSP2000), Sidney,
Australia, 2000.
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S. J. Godsill, A. Doucet, and M. West.
Maximum a posteriori sequence estimation using Monte Carlo particle
filters
. Ann. Inst. Stat. Math., 52(1), 2001
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S. J. Godsill, A. Doucet, and M. West.
Methodology for Monte Carlo smoothing with application to time-varying
autoregressions
. Symposium on Frontiers of Time Series Modelling, Institute of
Statistical Mathematics, Tokyo, 2000.
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Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan.
The Unscented Particle Filter.
Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering,
May 2000.
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Vasanth Philomin, Ramani Duraiswami, Larry Davis.
Quasi-Random Sampling for Condensation.
Proceedings ECCV, 2000.
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E. Punskaya, C. Andrieu, A. Doucet and W.J. Fitzgerald.
Particle Filtering for Optimal Detection in Fading Channels.
Technical report CUED/F-INFENG/TR 384, Cambridge University Department of Engineering, 2000.
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1999
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Christophe Andrieu, Nando de Freitas, Arnaud Doucet.
Sequential Bayesian Estimation and Model Selection Applied to Neural Networks.
Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering,
May 1999.
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Christophe Andrieu, Nando de Freitas, Arnaud Doucet.
Sequential MCMC for Bayesian Model Selection.
IEEE Signal Processing Workshop on Higher
Order Statistics. Ceasarea, Israel, June 14-16. 1999.
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Erik Bølviken, Geir Storvik, Fredrik Glockner (1999)
Deterministic and stochastic particle filters in state space models.
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T.C. Clapp and S.J Godsill.
Fixed-lag blind equalization and sequence estimation in digital communications
systems using sequential importance sampling.
In Proc. IEEE International Conference on Acoustics, Speech and Signal
Processing, volume 5, pages 2495-2498, March 1999.
Arizona.
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T.C. Clapp and S.J. Godsill.
Fixed-lag smoothing using sequential importance sampling.
In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, editors.
Bayesian Statistics VI, pp. 743-752. Oxford University Press,
1999.
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Crisan D., Del Moral P. and Lyons T.
Discrete Filtering Using Branching and Interacting Particle
Systems. To appear in Markov Processes and Related Fields , Volume 3, 1999.
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Del Moral P., Ledoux M.
On the Convergence and the Applications of Empirical Processes
for Interacting Particle Systems
and Nonlinear Filtering.
To appear in Journal of Theoret. Probability (1999).
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A. Doucet, N.J. Gordon and V. Krishnamurthy.
Particle Filters for State Estimation of Jump Markov Linear Systems.
Technical report CUED/F-INFENG/TR 359, Cambridge University Department
of Engineering, 1999.
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Richard Everson and Stephen J. Roberts (1999).
Non-stationary Independent Component Analysis.
Technical Report TR-99-1. March 1999. To appear in proceedings of
ICANN-99
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Paul Fearnhead (1999).
Particle filters for change-point detection.
abstract.
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D. Fox, W. Burgard, F. Dellaert, and S. Thrun, 1999.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
. In Proceedings of AAAI-99.
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A. Lanterman.
Tracking and recognition of airborne targets via commercial television and FM radio signals
Acquisition, Tracking, and Pointing XIII, SPIE Proc. 3692 , Orlando, FL, April 1999.
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Jane Liu and Mike West (1999).
Combined parameter and state estimation in
simulation-based filtering. In "Sequential Monte Carlo Methods in Practice,"
(eds: A. Doucet et al), (to appear).
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Mike Pitt and Neil Shephard.
Filtering
via simulation: auxiliary particle filter. Forthcoming Journal of
the American Statistical Association, 1999.
- S. Thrun, J. Langford, and D. Fox, 1999.
Monte Carlo
Hidden Markov Models: Learning Non-Parametric Models of Partially
Observable Stochastic Processes. In Proceedings of ICML-99.
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J. Vermaak, C. Andrieu, A. Doucet and S.J. Godsill.
On-line Bayesian modelling and enhancement of speech signals.
Technical Report CUED/F-INFENG/TR.361, Cambridge University Engineering
Department, Cambridge, England, 1999.
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1998...
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Nando de Freitas, Mahesan Niranjan, Andrew Gee and Arnaud Doucet.
Sequential Monte Carlo
Methods for Optimisation of Neural Network Models.
Technical report CUED/F-INFENG/TR 328, Cambridge University Department
of Engineering, July 1998.
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Arnaud Doucet.
On Sequential Simulation-Based Methods for Bayesian
Filtering.
Technical report CUED/F-INFENG/TR 310, Cambridge University Department
of Engineering, 1998. To appear in Statistics and Computing.
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Michael Isard and Andrew Blake.
ICondensation: Unifying low-level and high-level tracking in a stochastic framework.
Proc 5th European Conf. Computer Vision, Vol. 1 893-908, 1998
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Michael Isard and Andrew Blake.
A smoothing filter for Condensation.
Proc 5th European Conf. Computer Vision, Vol. 1 767-781, 1998.
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Michael Isard and Andrew Blake.
CONDENSATION
- conditional density propagation for visual tracking
Int. J. Computer Vision, 29, 1, 5--28, (1998).
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Jun Liu and Rong Chen.
Sequential Monte Carlo Methods for Dynamic Systems J. Amer. Statist. Assoc., Vol 93 in press.
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Mike West (1993).
Mixture models, Monte Carlo, Bayesian updating and
dynamic models. In "Computing Science and Statistics," Vol 24, 325-333.
Comments? Ideas? Do not hesitate to email: op205@eng.cam.ac.uk
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