2002

• Nando de Freitas. Rao-Blackwellised Particle Filtering for Fault Diagnosis. IEEE Aerospace, 2002. Available in PDF PS

2001

• 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.
• Cemgil, A.T. and Kappen, B. Rhythm Quantization and Tempo Tracking by Sequential Monte Carlo. Advances in NIPS, 2001.
• Yukito Iba. Population Monte Carlo algorithms. Transactions of the Japanese Society for Artificial Intelligence, vol.16, no.2, pp.279-286, 2001.
• 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.
• Dirk Ormoneit, Christiane Lemieux and David Fleet. Lattice Particle Filters. UAI, 2001.
• 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.
• Matthew Orton and Alan Marrs. Incorporation of Out-of-Sequence Measurements in Non-Linear Dynamic Systems using Particle Filters. Technical report, 2001.
• 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.

2000

• 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.
• Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Uncertainty in Artificial Intelligence (UAI2000).
• A. Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Comp., 10:197-208, 2000.
• 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.
• 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.
• 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
• 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.
• 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.
• Vasanth Philomin, Ramani Duraiswami, Larry Davis. Quasi-Random Sampling for Condensation. Proceedings ECCV, 2000.
• 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.

1999

• 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.
• 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.
• Erik Bølviken, Geir Storvik, Fredrik Glockner (1999) Deterministic and stochastic particle filters in state space models.
• 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.
• 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.
• 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.
• 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).
• 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.
• 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
• Paul Fearnhead (1999). Particle filters for change-point detection. abstract.
• D. Fox, W. Burgard, F. Dellaert, and S. Thrun, 1999. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots . In Proceedings of AAAI-99.
• 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.
• 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).
• 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.
• 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.

1998...

• 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.
• 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.
• 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
• Michael Isard and Andrew Blake. A smoothing filter for Condensation. Proc 5th European Conf. Computer Vision, Vol. 1 767-781, 1998.
• Michael Isard and Andrew Blake. CONDENSATION - conditional density propagation for visual tracking Int. J. Computer Vision, 29, 1, 5--28, (1998).
• Jun Liu and Rong Chen. Sequential Monte Carlo Methods for Dynamic Systems J. Amer. Statist. Assoc., Vol 93 in press.
• Mike West (1993). Mixture models, Monte Carlo, Bayesian updating and dynamic models. In "Computing Science and Statistics," Vol 24, 325-333.