Sequential Monte Carlo Methods

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(Date of publication)

2009

2008

2007

• Angelova, D. and Mihaylova, L., Extended Object Tracking Using Monte Carlo Methods, IEEE Trans. Signal Processing, In Print.
  
• Brasnett. P. Mihaylova, L., Bull, D. and Canagarajah,  N. Sequential Monte Carlo Tracking by Fusing Multiple Cues in Video Sequences. Image and Vision Computing, Elsevier Science, Vol. 28, No. 1, 2007, p. 1217-1227.
• Caron, F.,  Davy, M.,  Duflos, E. and Vanheeghe,  Ph., Particle Filtering for Multisensor Data Fusion with Switching Observation Models. Application to Land Vehicle Positioning. IEEE Trans. Signal Processing. To Appear. 2007
• Caron, F., Davy, M., Doucet, A., Duflos, E. and Vanheeghe, Ph. Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures. IEEE Trans. Signal Processing. To Appear. 2007.
• Chen, L., Lee, C., Budhiraja, A., and Mehra, R. K., PFlib: an object oriented MATLAB toolbox for particle filtering. Proceedings of SPIE Signal Processing , Sensor Fusion and Target Recognition XVI, V9olume 6567, 65670S, 2007.
• Dubois, C. and Davy, M. Joint Detection and Tracking of Time-Varying Harmonic Components: a Flexible Bayesian Approach. IEEE Trans. Speech, Audio and Language Processing, Volume 15, Issue 4, May 2007. pp 1283-1295.
• Fearnhead, P. Computational Methods for Complex Stochastic Systems: Alternatives to MCMC. Preprint. 2007.
• Fearnhead, P. Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. To Appear in Statistics and Computing.
• Fearnhead, P. and Liu, Z., Online Inference for Multiple Change Points Problems. To appear in JRSS Series B.
• Fernández-Prades, C., Closas, P. and Fernández-Rubio, J.A. Rao-Blackwellized Variable Rate Particle Filtering for Handset Tracking in Communication and Sensor Networks. EUSIPCO\'07 European Signal Processing Conference. September 2007. Poznan, Poland.
• Hegyi, A., Mihaylova, L., Boel, R., and Lendek, Zs. Parallelizable Particle Filtering for Freeway Traffic State Estimation. Proceedings of the European Control Conference, Greece, July 2-5, 2007, pp 2442-2449.
• Hoey, J., Tracking Using Flocks of Features, with Application to Assisted Handwashing. In Proceedings of British Machine Vision Conference (BMVC) 2006, Edinburgh, Scotland.
• Hu, X-L., Schön, T.B. and Ljung, L.  A Basic Convergence Result for Particle Filtering. Proceedings of the 7th IFAC Symposium on Nonlinear Control Systems (NOLCOS), Pretoria, South Africa, 2007.
• Hu, X-L., Schön, T.B. and Ljung, L. A Robust Particle Filter for State Estimation - with Convergence Results. Proceedings fo the 46th Conference on Decision and Control (CDC), New Orleans, LA, USA, December 2007.
• Johansen, A.M. and Doucet, A., Auxiliary variable sequential Monte Carlo methods, University of Bristol Technical Report #07-09, 2007.
• Johansen, A.M., Doucet, A., and Davy, M. Particle methods for maximum likelihood parameter estimation in latent variable models. Statistics and Computing, 2007. To appear.
• Mihaylova, L., Angelova, D. Honary,S., Bull, D. Canagarajah,  N, and Ristic, B. Mobility Tracking in Cellular Networks Using Particle Filtering. IEEE Trans. Wireless Communications, Vol. 6, No. 10, 2007.
  
• Mihaylova, L., Boel, R. and Hegyi, A. Freeway Traffic Estimatino within Recursice Bayesian Framework. Automatica, Vol 43, No. 2 February 2007, p. 290-300.
• Najim, K., Ikonen, E. and Del Moral, P., Open-loop regulation and tracking control based on a genealogical decision tree, Neural Computing and Applications, Volume 15, Issue 3, pp. 339-349, May 2006.
• Nix, J. and Hohmann, V., Combined estimation of spectral envelopes and sound source direction of concurrent voices by multidimensional statistical filtering, IEEE Trans. Audio, Speech and Lang. Proc. 15(3), p. 995-1008.
• Peters, G., Johansen, A., and Doucet, A., Simulation of the Annual Loss Distribution in Operational Risk via Panjer Recursions and Volterra Integral Equations for Value at Risk and Expected Shortfall Estimation. Journal of Operational Risk 2(3). pp 1-30. Fall 2007
• Särkkä, S., Vehtari, A., and Lampinen, J., Rao-Blackwellized Particle Filter for Multiple Target Tracking. Information Fusion Journal,  Volume 8, Issue 1, Pages 2-15, January 2007.
• Septier, F. , Delignon, Y., Menhaj-Rivenq, A., and Garnier, C. OFDM channel estimation in the presence of phase noise and frequency offset by particle filtering. Proceedings of IEEE ICASSP 2007, Honolulu, Hawaii.
• Schön, T.B., Karlsson, R., Törnqvist, D. and Gustafsson, F. A Framework for Simultaneous Localization and Mapping Utilizing Model Structure. Proceedings of the 10th international Conference on Information Fusion, Quebec, Canada, July 2007.
• Schön, T.B., Törnqvist, D. and Gustafsson, F. Fast particle filters for multi-rate sensors. Proceedings of 15th European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007.
• Singh, J., Kumar, R., Madhow, U. , Suri,S.,  Cagley, R.,  Tracking Multiple Targets Using Binary Proximity Sensors. Information Processing in Sensor Netorks (IPSN07), 2007.
• Sisson S. A., Y. Fan and M. M. Tanaka , Sequential Monte Carlo without Likelihoods. Proc. Natl. Acad. Sci. USA. In press.
• Wang, Y-D., Wu, J-K., Kassim, A. Adaptive Particle Filter for Data Fusion of Multiple Cameras. Journal of VLSI Signal Processing Systems. Volume 49, Number 3. December 2007.
• Whiteley, N., Johansen, A.M. and Godsill, S. Efficient Monte Carlo Filtering for Discretely Observed Jumping Processes. To appear in Proceedings of IEEE SSP 2007.
• Whiteley, N., Singh, S. and Godsill, S. Auxiliary Particle Implementation of the Probability Hypothesis Density Filter. To appear in Proceedings of IEEE ISPA 2007.
• Whiteley, N., Singh, S. and Godsill, S. Auxiliary Particle Implementation of the Probability Hypothesis Density Filter. Cambridge University Engineering Department Technical ReportCUED/F-INFENG/TR-592, December 2007.
  
• Whiteley, N., Johansen, A.M. and Godsill, S. Monte Carlo Filtering of Piecewise Deterministic Processes. Cambridge University Engineering Department Technical Report CUED/F-INFENG/TR-590, December 2007.
• Xu, X. and LI, B. Adaptive Rao-Blackwellised Particle Filter and its Evaluation for Tracking in Surveillance. IEEE Transactions on Image Processing, Volume 16, Issue 3, March 2007, p 838-849.

2006

• Angelova, D., and Mihaylova, L., Joint Target Tracking and Classification with Particle Filtering and Mixture Kalman Filtering Using Kinematic Radar Information, Digital Signal Processing, Elsevier Science, Vol. 16, No. 2, pp. 180-204, 2006.
• Closas, P., Fernández-Prades, C., Fernández-Rubio, J.A., Ramírez-González, A., Multipath Mitigation using Particle Filtering, ION'06 Institute Of Navigation Satellite Division Technical Meeting, ION GNSS. Fort Worth, Texas, USA. September, 2006.
• Closas, P., Fernández-Prades, C., and Fernández-Rubio, J.A., Optimizing the Likelihood with Sequential Monte-Carlo methods, URSI'06 XXI Simposium Nacional de la Unión Científica Internacional de Radio. Oviedo, Spain. September, 2006.
• Closas, P., Fernández-Prades, C., and Fernández-Rubio, J.A., Particle Filtering applied to Robust Multivariate Likelihood Optimization in the absence of a closed-form solution, NSSPW'06 Nonlinear Statistical Signal Processing Workshop. Cambridge, UK. September, 2006.
• Closas, P., Fernández-Prades, C., and Fernández-Rubio, J.A. Bayesian DLL for Multipath Mitigation in Navigation Systems using Particle Filters, ICASSP'06 International Conference on Acoustics, Speech, and Signal Processing. May 2006. Toulouse, France.
• Closas, P., Fernández-Prades, C., and Fernández-Rubio, J.A., Sequential Monte-Carlo approximation to the ML Time-delay Estimator in a Multipath Channel, SPAWC'06 Proceedings of the IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.
• Doucet, A., Briers, M., Senecal, S., Efficient Block Sampling Strategies for Sequential Monte Carlo. To appear J. Comp. Graph. Statist.
• Del Moral, P., Doucet A., and Jasra, A., Sequential Monte Carlo Samplers. To appear J. Royal Statist. Soc. B
• Gall J., Rosenhahn B., Brox T., and Seidel H.-P., Learning for Multi-View 3D Tracking in the Context of Particle Filters, International Symposium on Visual Computing (ISVC'06), Springer, LNCS 4292, 59-69, 2006.
• Gustafsson, F., Schön, T.B., Karlsson R., and Nordlund, P-J., State-of-the-Art for the Marginalized Particle Filter, Nonlinear Statistical Signal Processing Workshop, Cambridge, United Kingdom, September 2006.
• Hol, J.D., Schön, T.B., Gustafsson, F., Resampling in Particle Filters, Nonlinear Statistical Signal Processing Workshop, Cambridge, United Kingdom, September 2006.
• Johansen, A.M., Del Moral, P., and Doucet, A., Sequential Monte Carlo samplers for rare events, In Proceedings of the 6th International Workshop on Rare Event Simulation, Bamberg, Germany, October 2006.
• Johansen, A. M., Doucet, A., and Davy, M., Maximum likelihood parameter estimation for maximum likelihood models using sequential Monte Carlo. To appear Proceedings of ICASSP.
• Klass, M., Briers, M., de Freitas, N., Doucet, A., Maskell, S.R., Lang, D., Fast Particle Smoother: If I Had a Million Particles, 23rd International Conference on Machine Learning.
• Lombardi, M., and Godsill, S.J., On-line Bayesian Estimation of AR Signals in Symmetric alpha-Stable Noise, IEEE Tr. Signal Processing, Feb. 2006.
• Polson, N. G., Stroud, J. R., and Müller, P., Practical Filtering with Sequential Parameter Learning, University of Pennsylvania Working Paper, 2006.
• Poyadjis G., Singh S.S., and Doucet A., On Line Parameter Estimation for Partially Observed Diffusions, Nonlinear Statistical Signal Processing Workshop, Cambridge, United Kingdom, September 2006.
• Särkkä, S., Recursive Bayesian Inference on Stochastic  Differential Equations, Doctoral dissertation, April 2006.
  
• Särkkä, S., On Sequential Monte Carlo Sampling of Discretely  Observed Stochastic Differential Equations. In Proceedings of NSSPW,  Cambridge, September 2006.
• Schön, T.B., Estimation of Nonlinear Dynamic Systems - Theory and Applications, PhD Thesis, Linkoping University, Linkoping, Sweden.
• Schön, T.B., Karlsson, R., and Gustafsson, F., The Marginalized Particle Filter in Practice, Proceedings 2006 IEEE Aerospace Conference, Big Sky, USA, Mar. 2006.
• Schön, T.B., Wills, A., and Ninness, B., Maximum Likelihood Nonlinear System Estimation, Proceedings 14th IFAC Symposium on System Identification, pages 1003-1008, Newcastle, Australia, Mar. 2006.
• Septier, F., Delignon, Y., Menhaj-Rivenq, A., and Garnier, C., Particle filtering with hybrid importance function for joint symbol detection and phase tracking, SPAWC'06 Proceedings of the IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.
• Shabany, M., Gulak, P. G., An Efficient Architecture for Distributed Resampling for High-Speed Particle Filtering,  IEEE International Symposium on Circuits and Systems (ISCAS'06), Greece, May 21-24, 2006.
• Shabany, M., Gulak, P.G., VLSI Implementation of a Sequential Monte Carlo Receiver, IEEE International Symposium on Circuits and Systems (ISCAS'06), Greece, May 21-24, 2006.
• Vaswani, N., Yezzi, A.,  Rathi, Y., Tannenbaum, A. Time-varying Finite Dmensional Basis for Tracking Contour Deformations. Proceedings of IEEE Conference on Decision and Control (CDC), 2006.
• Vaswani, N., Yezzi, A., Rathi, Y., Tannenbaum, A., Particle Filters for Infinite (Or Large) Dimensional State Spaces - Part 1,  IEEE Intl. Conference on Acoustics, Speech and Signal Processing  (ICASSP), 2006.
• Vaswani, N., Particle Filters for Infinite (Or Large) Dimensional State Spaces - Part 2, IEEE Intl. Conference on Acoustics, Speech and Signal Processing  (ICASSP), 2006.
• Vihola, M. Rao-Blackwellised Particle Filtering in Random Set Multitarget Tracking. To appear in IEEE Transactions on Aerospace and Electronic Systems.

2005

• Andrieu C., Doucet A. and Tadic V.B., Online simulation-based methods for parameter estimation in non linear non Gaussian state-space models, Proc. IEEE CDC .
• Sileye O. Ba and Jean-Marc Odobez A Rao-Blackwellized Mixed State Particle Filter for Head Pose Tracking in Meetings /conference: ACM ICMI Workshop on Multimodal Multiparty Meeting Processing (MMMP), Trento Italy October 7, 2005
• Briers M, Doucet A and Singh S.S., Sequential auxiliary particle belief propagation, Proc. Fusion.
• Chopin, N., Sequential Monte Carlo for estimation and state number determination in hidden Markov models.
• Eidehall, A., Schön, T.B., and Gustafsson, F., The Marginalized Particle Filter for Automotive Tracking Applications,  Proceedings of 2005 IEEE Intelligent Vehicle Symposium, pages 369-374, Las Vegas, USA, Jun. 2005.
• Gilholm, K., Godsill, S.J., Maskell, S.R., and Salmond D.J., Poisson models for extended target and group tracking.
• Godsill S.J., and Vermaak, J., Variable rate particle filters for tracking applications, In Proc. IEEE Stat. Sig. Proc., Bordeaux, 2005.
• Hamze F and De Freitas J.F.G., Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. NIPS 2005.
• Haug, A. J., A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes, MITRE Technical Report MTR 05W0000004, 2005.
• Johansen A. M., Singh S. S, Doucet A. and Vo B-N. Convergence of the SMC Implementation of the PHD Filter, Cambridge University Engineering Department Technical Report, CUED/F-INFENG/TR-517
• Karlsson, R., Schön, T.B., and Gustafsson, F., Complexity Analysis of the Marginalized Particle Filter, IEEE Transactions on Signal Processing, 53(11):4408-4411, Nov. 2005.
• Klaas M., De Freitas J.F.G. and Doucet A., Towards practical N^2 Monte Carlo: The marginal particle filter, Proc. UAI.
• Klaas M., Lang D. and De Freitas J.F.G., Fast Maximum a Posteriori Inference in Monte Carlo State Spaces . AISTATS.
• Poyadjis G, Doucet A and Singh S.S., Particle methods for optimal filter derivative: Application to parameter estimation, Proc. IEEE ICASSP .
• Peters, G. Topics in Sequential Monte Carlo Samplers. M.Sc. Thesis, University of Cambridge, UK.
• Pupilli, M., and Calway, A., Real-Time Camera Tracking Using a Particle Filter, In Proceedings of the British Machine Vision Conference, BMVA Press, September 2005.
• Schön, T.B., Gustafsson, F., and Nordlund, P-J., Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models, IEEE Transactions on Signal Processing, 53(7):2279-2289, Jul. 2005.
• Shabany, M., Shojania, H., Zhang, J., Omidi, J., Gulak, P. G., VLSI Architecture of a Wireless Channel Estimator Using Sequential Monte Carlo Methods, IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC'05), 2005.
• Singh S.S., Kantas N., Vo B-N., Doucet A., Evans R.J., Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning, Cambridge University Engineering Department Technical Report, CUED/F-INFENG/TR-517.
• Schubert, J. and Sidenbladh, H., Sequential clustering with particle filters - Estimating the number of clusters from data, in Proceedings of the Eighth International Conference on Information Fusion (FUSION 2005), Philadelphia, USA, 25-29 July 2005. IEEE, Piscataway, NJ, 2005, Paper A4-3,pp. 1-8.
• Vermaak, J., Ikoma, N., and Godsill, S.J., Sequential Monte Carlo framework for extended object tracking, IEE Proc.-Radar Sonar Navig., 152(5):353-363, October 2005.
• Vermaak, J., Godsill, S.J., and Perez, P. Monte Carlo filtering for multi-target tracking and data association, IEEE Tr. Aerospace and Electronic Systems, 41(1):309-332, January 2005.
• Vo B-N., Singh S.S., and Doucet A., Sequential Monte Carlo methods for Multi-target Filtering with Random Finite Sets, to appear in IEEE Aerospace and Electronic Systems.

2004

• Andrieu C., Doucet A., Singh S.S., and Tadic V.B., Particle Methods for Change Detection, System Identification, and Control, Proceedings of the IEEE, Vol 92, No 3, March 2004. 
• Angelova D. and Mihaylova L. Sequential Monte Carlo Algorithms for Joint Target Tracking and Classification Using Kinematic Radar Information. Proc. of the 7th Intl. Conf. on Information Fusion, Stockholm, Sweden, 2004.
• Angelova D., Mihaylova L. and Semerdjiev T. Monte Carlo Algorithm for Maneuvering Target Tracking and Classification. Lecture Notes in Computer Science, vol. 3039, pp. 531-539, 2004.
• Bolic M. Architectures for Efficient Implementation of Particle Filters. Ph.D. thesis, State University of New York at Stony Brook, 2004.
• Bolic M., Djuric P.M. and Hong S. Resampling Algorithms and Architectures for Distributed Particle Filters. IEEE Trans. on Signal Processing, to appear 2004.
• Fernandez-Villaverde J. and Rubio-Ramirez J.F. Estimating Nonlinear Dynamic Equilibrium Economies: A Likelihood Approach. 2004.
• Fernandez-Villaverde J. and Rubio-Ramirez J.F. Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood. 2004.
• Godsill S.J., and Doucet, A., and West, M., Monte Carlo smoothing for non-linear time series, Journal of the American Statistical Association. Vol.50, pp. 438-449, 2004.
• Godsill S.J., and Vermaak, J., Models and algorithms for tracking using trans-dimensional sequential Monte Carlo, In Proc. IEEE ICASSP 2004.
• Mihaylova L. and Boel R.. A Particle Filter for Freeway Traffic. 43rd IEEE Conf. on Decision and Control, pp. 2106-2111, 2004.
• Perez P., Vermaak J., and Blake A. Data fusion for visual tracking with particles. Proc. of IEEE, 92, 3, 495--513,2004.
• Rekleitis I. A Particle Filter Tutorial for Mobile Robot Localization. Technical Report TR-CIM-04-02, Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada, 2004.
• Ristic, B.,  Arulampalam, S. and Gordon, N. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. Boston, 2004.
• Sarkka S. and Vehtari A. and Lampinen J. Rao-Blackwellized Monte Carlo data association for multiple target tracking. In Proceedings of FUSION 2004 , The 7th International Conference on Information Fusion, Stockholm, June 2004.
• Vaswani N. Bound on Errors in Particle Filtering with Incorrect Model Assumptions and its Implication for Change Detection. ICASSP, 2004.
• Vaswani N. Change Detection in Partially Observed Nonlinear Dynamic Systems With Unknown Change Parameters. American Control Conference, 2004.

2003

• Andrieu C. and Doucet A. Online Expectation-Maximization Type Algorithms for Parameter Estimation in General State Space Models. Proc. IEEE ICASSP, 2003.
• Cappe O., Guillin A., Marin J.M., and Robert C.P. Population Monte Carlo for ion channel restoration. J. Comput. Graph. Stat., to appear.
• Caylus N., Guyader A. and LeGland F. Particle Filters for Partially Observed Markov Chains. Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, Saint Louis, September 29-October 1, 2003.
• Chan B., Doucet A. and Tadic V.B. Optimization of Particle Filters using Simultaneous Perturbation Stochastic Approximation. IEEE ICASSP, 2003.
• Chen Y. and Lai T. L. Sequential Monte Carlo Methods for Filtering and Smoothing in Hidden Markov Models. to appear.
• Chen M.Z. and Zhou D.H. Particle filtering based fault prediction of nonlinear systems. Proceedings of IFAC Symposium Processing of Safeprocess, June 2003.
• Chopin, N. Sequential inference and state number determination for discrete state-space models through particle filtering. CREST working paper 2001-34, 2001.
• Celeux G., Marin J.M. and Robert C.P. Iterated importance sampling in missing data problems.
• Cemgil A. T. and Kappen H. J. Monte Carlo methods for tempo tracking and rhythm quantization. Journal of Artificial Intelligence Research, 18:45-81, 2003.
• Del Moral P. and Doucet A. On a Class of Genealogical and Interacting Metropolis Models. Seminaire de Probabilites XXXVII, Ed. J. Azema and M. Emery and M. Ledoux and M. Yor, Lecture Notes in Mathematics, Springer-Verlag Berlin, to appear 2003.
• Doucet A. and Tadic V.B. Parameter Estimation in General State-Space Models using Particle Methods. Annals of the Institute of Statistical Mathematics, to appear 2003.
• Evensen G. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation. Ocean Dynamics, 53, 343-367, 2003.
• Gatica-Perez D., Lathoud G., McCowan I., and Odobez J.-M. A mixed-state i-particle filter for multi-camera speaker tracking. Proc. IEEE ICCV Workshop on Multimedia Technolgies for Learning and Collaboration (ICCV-WOMTEC), Nice, Oct. 2003.
• Guyader A. LeGland F. and Oudjane N. A Particle Implementation of the Recursive MLE for Partially Observed Diffusions. Proceedings of the 13th IFAC / IFORS Symposium on System Identification, Rotterdam, August 27-29, 2003.
• Iltis R.A. A Sequential Monte Carlo Filter for Joint Linear/Nonlinear State Estimation with Application to DS-CDMA . IEEE Transactions on Signal Processing, to appear.
• Haehnel D., Burgard W., Fox D., and Thrun S. An Efficient FastSLAM Algorithm for Generating Maps of Large-scale Cyclic Environments From Raw Laser Range Measurements. IROS-03, 2003.
• Johannes M. S., Polson N. G. and Stroud J. R. Sequential Parameter Estimation in Stochastic Volatility Jump-Diffusion Models. 2003.
• Karlsson R., Gustafsson F. Particle Filter for Underwater Terrain Navigation. Department of Electrical Engineering, Linkoping University, LiTH-ISY-R-2530, 2003.
• Karlsson R., Gustafsson F. and Karlsson T. Particle filter and Cramer-Rao lower bound for underwater navigation. ICASSP, Hongkong, PRC, 2003.
• Karlsson R., Jansson J., Gustafsson F. Model-based Statistical Tracking and Decision Making for Collision Avoidance Application. Department of Electrical Engineering, Linkoping University, LiTH-ISY-R-2492, 2003.
• Khan Z., Balch T., and Dellaert F. Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model. Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’03), 2003. Uses MCMC instead of importance sampling to enable multi-target tracking..
• Kitagawa G., Higuchi T., and Kondo. F. N. Smoothness Prior Approach to Exolore Mean Structure in Laege Time Series. Theoretical Computer Science, 292, No.2, 431-446, 2003. Also available in ps.
• Kwok C., Fox D., and Meila M. Adaptive Real-time Particle Filters.ICRA, 2003.
• LeGland F. and Oudjane N. A Robustification Approach to Stability and to Uniform Particle Approximation of Nonlinear Filters : the Example of Pseudo-Mixing Signals. Stochastic Processes and their Applications, vol. 106, 2, pp. 279-316, August 2003.
• LeGland F. and Oudjane N. Stability and Uniform Approximation of Nonlinear Filters using the Hilbert Metric, and Application to Particle Filters. The Annals of Applied Probability, to appear.
• Liao L., Fox D., Hightower J., Kautz H., and Schulz D. Voronoi Tracking: Location Estimation Using Sparse and Noisy Sensor Data. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. Abstract.
• Nummiaro K., Koller-Meier E. B. and Van Gool L. Color Features for Tracking Non-Rigid Objects. Special Issue on Visual Surveillance, ACTA Automatica Sinica (Chinese Journal of Automation), 2003.
• Rekleitis I.M., Dudek G., and Milios E. Probabilistic Cooperative Localization and Mapping in Practice. Proceedings of IEEE International Conference in Robotics, 2003.
• Schon T. On Computational Methods for Nonlinear Estimation. Licentiate Thesis no. 1047, Linkoping University, Oct 2003.
• Schon T. and Gustafsson F. Particle filters for system identification of state-space models linear in either parameters or states. Proceedings of the 13th IFAC Symposium on System Identification, pp. 1287-1292, 2003.
• Schulz D., Burgard W., Fox D., and Cremers A.B. People Tracking with a Mobile Robot Using Sample-based Joint Probabilistic Data Association Filters. International Journal of Robotics Research (IJRR), 2003.
• Schulz D., Fox D., and Hightower, J. People Tracking with Anonymous and ID-sensors Using Rao-Blackwellised Particle Filters. International Joint Conference on Artificial Intelligence, 2003. Abstract.
• Sidenbladh H. and Black M.J. Learning the statistics of people in images and video. International Journal of Computer Vision, 54(1-3):183-209, 2003.
• Sidenbladh H. Multi-target particle filtering for the probability hypothesis density. International Conference on Information Fusion, pp 800-806, Cairns, Australia 2003.
• Sidenbladh H. and Wirkander S.-L. Tracking random sets of vehicles in terrain. IEEE Workshop on Multi-Object Tracking, Madison, WI, USA 2003.
• Skrivanek Z., Lin S. and Irwin M. E . Linkage Analysis with Sequential Imputation. Genetic Epidemiology, 25: 25-35, 2003.
• Srivastava A. and Klassen E.. Geometric Filtering for Subspace Tracking. Advances in Applied Probability, 36(1), to appear March 2004.
• Stroud, J. R., Polson N. G. and Muller P. Practical Filtering for Stochastic Volatility Models. 2003.
• Tindel S. and Viens F. Convergence of a branching particle system to the solution of a stochastic PDE. Rand. Operators Stoch. Eqs., to appear 2003.
• Torma P., Szepesvari Cs. Sequential Importance Sampling for Visual Tracking Reconsidered. Proc. AI and Statistics, 2003.
• Van Leeuwen P.J. A variance-minimizing filter for large-scale applications. Monthly Weather Review, 131, p.2071-2084, 2003.
• Vaswani N., Chelleppa R. A Particle Filtering Approach to Abnormality Detection in Nonlinear Systems and its Application to Abnormal Activity Detection. 3rd Intl. Workshop on Statististical and Computational Theories of Vision (SCTV), to be held along with ICCV, 2003.
• Vaswani N., Roy Chowdhury A., Chelleppa R. Activity Recognition Using the Dynamics of the Configuration of Interacting Objects. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003.
• Vermaak, J., Godsill, S.J., and Doucet, A., Radial basis function regression using trans-dimensional sequential Monte Carlo, In IEEE Workshop on Statistical Signal Processing, 2003.
• Vermaak, J., Godsill, S.J., and Doucet, A., Sequential Bayesian kernel regression, In Advances in Neural Information Processing Systems 16, Cambridge, MA. MIT Press, 2003.
• Vo B.-N., Singh S. and Doucet A. Sequential Monte Carlo Implementation of the PHD Filter for Multi-target Tracking. Proc. FUSION 2003, pp. 792-799, Cairns, Australia, 2003.
• Zhang J., Chen R., Tang C. and Liang J. Origin of scaling behavior of protein packing density: A sequential Monte Carlo study of compact long chain polymers. J. Chem. Phys., 118(13): pp. 6102-6109, 2003.

2002

• Andrieu C. and Doucet A.Particle Filtering for Partially Observed Gaussian State Space Models.J. Royal Statist. Soc. B (Methodological), vol.64, no. 4, pp. 827-836, 2002.
• Arulampalam, M.S., Maskell, S., Gordon, N. and Clapp, T. A Tutorial on Particle Filters for nonlinear/non-Gaussian Bayesian Tracking. IEEE Trans. Signal Processing, Vol. 50, No. 2, 2002. p.174-188.
• Azimi-Sadjadi B. and Krishnaprasad P. S.Approximate Nonlinear Filtering and Its Applications for an Integrated INS/GPS. Automatica, submitted 2002.
• Azimi-Sadjadi B. and Krishnaprasad P. S.Change Detection for Nonlinear Systems: A Particle Filtering Approach. Proceedings of 2002 American Control Conference, 2002.
• Bui H.H., Venkatesh S., West G. Policy recognition in the Abstract Hidden Markov Models. Journal of Artificial Intelligence Research, Vol. 17, pp. 451-499, 2002.
• Chib S., Nardari F. and Shephard N. Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics, 108, pp. 281-316, 2002.
• Chin W. H., Ward D. B., and Constantinides A. G.Channel Tracking for Space-Time Block Coded Systems using Particle Filtering. in Proc. DSP 2002, vol. 2, pp. 671-674, Jul 2002.
• Chin W. H., Ward D. B., and Constantinides A.G. Semi-blind Channel Tracking Using Auxiliary Particle Filtering.in IEEE GLOBECOM, vol. 1, pp. 322-325, Nov 2002.
• Chopin, N. A sequential particle filter method for static models. Biometrika, 89, 539-552, 2002. An older version featuring full proof of Theorem 1 is available as [postscript].
• Cemgil A. T. and Kappen H. J. Integrating tempo tracking and quantization using particle filtering. Proceedings of the 2002 International Computer Music Conference, pp. 419-422, Gothenburg/Sweden, 2002.
• Cemgil A. T. and Kappen H. J. Rhythm quantization and tempo tracking by Sequential Monte Carlo. Advances in Neural Information Processing Systems 14, pp. 1361-1368. MIT Press, 2002.
• Cerou F., Del Moral P., Le Gland F. and Lezaud P. Genealogical Models in Rare Event Analysis. Publications du Laboratoire de Statistiques et Probabilites, Toulouse III, 2002.
• Coates M. J. and Nowak R. Sequential Monte Carlo Inference of Internal Delays in Nonstationary Data Networks. IEEE Transactions on Signal Processing, Special Issue on Monte Carlo Methods for Statistical Signal Processing, vol . 50, no. 2, pp. 366-376, Feb. 2002.
• Crisan D.Exact Rates of Convergence for a Branching Particle Approximation to the Solution of the Zakai Equation.The Annals of Probability, to appear.
• Crisan D. and Doucet A.. A Survey of Convergence Results on Particle Filtering for Practitioners. IEEE Trans. Signal Processing, vol. 50, no. 3, pp. 736-746, 2002.
• Crisan D. and Lyons T.Minimal Entropy Approximations and Optimal Algorithms for the Filtering Problem.Monte Carlo Method and Applications, vol. 8, no. 4, pp. 343-356, 2002.
• de Freitas N.Rao-Blackwellised Particle Filtering for Fault Diagnosis. IEEE Aerospace, 2002. Available also in PS
• Denzler J., Zobel M., and Triesch J. Probabilistic Integration of Cues from Multiple Cameras. 4. Workshop Dynamic Perception, Bochum, Germany, 2002.
• Dowd M. and Meyer R. A Bayesian Approach to the Ecosystem Inverse Problem with Application to a Shellfish Growth Model. submitted, August 2002.
• Fearnhead P. and Clifford P. Online inference for well-log data. Submitted.
• Fearnhead P. MCMC, sufficient statistics and particle filter. Journal of Computational and Graphical Statistics, vol. 11, pp. 848-862, 2002.
• Fearnhead P. Particle filters for mixture models with an unknown number of components. Submitted.
• Forssell U., Hall P., Ahlqvist S. and Gustafsson F. Novel Map-Aided Positioning System. Proc. of FISITA, Helsinki, Finland, 2002.
• Grassberger P. Go with the Winners: a General Monte Carlo Strategy. Computer Physics Communications (Proceedings der CCP2001, Aachen 2001); cond-mat/0201313, 2002.
• Gustafsson F., Gunnarsson F., Bergman N., Forssell U., Jansson J., Karlsson R. and Nordlund P.-J. Particle filters for positioning, navigation and tracking. IEEE Transactions on Signal Processing, vol. 50, No. 2, 2002.
• Hahnel D., Schulz D., Burgard W.. Map Building with Mobile Robots in Populated Environments. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2002.
• Herman S. M. A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification. PhD thesis, Univ. of Illinois at Urbana-Champaign, 2002.
• Herman S. M. and Moulin P. A Particle Filtering Approach to Joint Radar Tracking and Automatic Target Recognition. Proc. IEEE Aerospace Conference Big Sky, Montana, March 10-15, 2002.
• Hsu H.-P., Mehra V., Nadler W., Grassberger P. A Growth-based Optimization Algorithm for Lattice Heteropolymers. E-print cond-mat/0209366 2002.
• Hsu H.-P., Mehra V., Nadler W., Grassberger P. Growth Algorithms for Lattice Heteropolymers at Low Temperatures. E-print cond-mat/0208042 2002.
• Hue C., Le Cadre J.-P., Perez P. Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multi-target tracking. 5th International Conference on Information Fusion, FUSION'2002, pp. 464-473, Annapolis, Maryland, 2002.
• Hue C., Le Cadre J.-P., Perez P. Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multi-target tracking. Rapport de recherche IRISA, No 1457, 2002.
• Hue C., Le Cadre J.-P., Perez P. Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Trans. on Signal Processing, 50(2):309-325, 2002.
• Hue C., Le Cadre J.-P., Perez P. Tracking multiple objects with particle filtering. IEEE Trans. on Aerospace and Electronic Systems, 38(3):791-812, 2002.
• Ikoma N., Higuchi T., and Maeda H. Maneuvering target tracking by using particle filter method with model switching structure. Conference for Computational Statistics (Compstat 2002), Berlin, Germany, Aug.24-28, 2002.
• Ikoma N., Higuchi T., and Maeda H. Tracking of maneuvering target by using switching structure and heavy-tailed distribution with particle filter method. IEEE Conference on Control Applications, Glasgow, Scotland, Sep.18-20, 2002.
• Irwin M. E., Cressie N., and Johannesson G. Spatial-temporal nonlinear filtering based on hierarchical statistical models (with discussion). Test, 11:249-302, 2002.
• Irwin M. E., Cressie N., and Johannesson G. Spatial-temporal nonlinear filtering in Command and Control (C2). Technical Report No. 691, Department of Statistics, The Ohio State University, 2002. Preprint available from the SSES preprints service.
• Jansson J., Johansson J. and Gustafsson F. Decision making for collision avoidance systems. SAE 2002, Detroit, Report number 2002-01-0403, 2002.
• Johannes M. S., Polson N. G. and Stroud J. R. Nonlinear Filtering of Stochastic Differential Equations with Jumps. 2002.
• Johannes M. S., Polson N. G. and Stroud J. R. Sequential Optimal Portfolio Performance: Market and Volatility Timing. 2002.
• Karlsson R. Simulation Based Methods for Target Tracking. PhD thesis, Department of Electrical Engineering, Linkoping University, 2002. Also available as ps.gz
• Karlsson R. Various Topics on Angle-Only Tracking using Particle Filters. Department of Electrical Engineering, Linkoping University, LiTH-ISY-R-2473, 2002.
• Kim S.-J. and Iltis R.A. Performance Comparison ofParticle and Extended Kalman Filter Algorithms for GPS C/A Code Tracking and Interference Rejection. Proc. of CISS 2002, Princeton, NJ, March, 2002.
• Koutsoukos X., Kurien J., and Zhao F. Monitoring and Diagnosis of Hybrid Systems Using Particle Filtering Methods. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems - MTNS 2002, Notre Dame, IN, August 2002.
• Kwok C., Fox D., and Meila M. Real-time Particle Filters. NIPS, 2002.
• Larocque J.-R., Reilly J. P. and Ng W. Particle Filters for Tracking an Unknown Number of Sources. IEEE Transactions on Signal Processing, Vol. 50, No. 12, pp. 2926-2937, 2002.
• Liang J., Zhang J. and Chen R. Statistical geometry of packing defects of lattice chain polymer from enumeration and sequential Monte Carlo method. J. Chem. Phys., 117:3511-3521, 2002.
• Magee D. and Boyle R. Detecting Lameness using `Re-sampling condensation' and `Multi-stream Cyclic Hidden Markov Models'. Image and Vision Computing, vol 20(8), pp 581-594, 2002. Available as a Research Report.
• Milstein A., Sanchez J.N., Williamson E.T. Robust Global Localization Using Clustered Particle Filtering . AAAI, , 2002.
• Montemerlo M., Whittaker W. and Thrun S. Conditional particle filters for simultaneous mobile robot localization and people-tracking. IEEE International Conference on Robotics and Automation (ICRA), Washington, DC, 2002.
• Nordlund P.-J., Gunnarsson F. and Gustafsson F. Particle filters for positioning in wireless networks. Proc. of EUSIPCO , Toulouse, France, September, 2002.
• Ng B., Peshkin L. and Pfeffer A. Factored Particles for Scalable Monitoring. UAI, 2002. Also available in ps.
• Nordlund P.-J. and Gustafsson F. Recursive estimation of three-dimensional aircraft position using terrain-aided positioning. Proc. of ICASSP, Orlando, May, 2002.
• Nummiaro K., Koller-Meier E. B. and Van Gool L. An Adaptive Color-based Particle Filter. Image and Vision Computing, Vol. 21, pp. 99-110, 2002.
• Nummiaro K., Koller-Meier E. B. Van Gool L. Object Tracking with an Adaptive Color-Based Particle Filter. Symposium for Pattern Recognition of the DAGM, pp. 355-360, 2002.
• Nummiaro K., Koller-Meier E. B. Van Gool L. A Color-Based Particle Filter. 1st International Workshop on Generative-Model-Based Vision GMBV'02, in conjuction with ECCV'02, pp. 53-60, 2002.
• Papavasiliou A. Adaptive Particle Filters with Applications. PhD thesis, Princeton, 2002.
• Papavasiliou A. Asymptotic Stability of the Optimal Filter for non-ergodic signals". submitted, 2002.
• Perez P., Hue C., Vermaak J. and Gangnet M. Color-based probabilistic tracking. Proc. Eur. Conf. on Computer Vision (ECCV), 2002.
• Pitt M. K. Smooth Particle Filters for Likelihood Evaluation and Maximisation. Warwick Economic Research Papers, No. 651, July 2002.
• Rekleitis I.M. and Dudek G. and Milios E.. Multi-Robot Cooperative Localization: A Study of Trade-offs Between Efficiency and Accuracy. IEEE/RSJ/ International Conference on Intelligent Robots and Systems, pp. 2690-2695, 2002. Also available as ps.gz.
• Ridgeway G. and Madigan D. A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets. JDMKD to appear, 2002.
• Ridgeway G. and Madigan D. Bayesian Analysis of Massive Datasets via Particle Filters. KDD-02, 2002.
• Storvik G. Particle filters in state space models with the presence of unknown static parameters. IEEE Trans. on Signal Processing, vol. 50, no. 2, pp. 281--289, 2002.
• Thrun S. Particle filters in robotics. Proceedings of the 17th Annual Conference on Uncertainty in AI (UAI), 2002.
• Torma P., Szepesvari Cs. Combining Local Search, Neural Networks and Particle Filters to Achieve Fast and Reliable Contour Tracking .
• Torma P., Szepesvari Cs. Towards Facial Pose Tracking. Proc. First Hungarian Computer Graphics and Geometry Conference, Budapest, Hungary, pp. 10-16, 2002 .
• Toyama K. and Blake A. Probabilistic tracking with exemplars in a metric space. Int. J. Computer Vision, in press, 2002.
• Vermaak J., Andrieu C., Doucet A. and Godsill S.J. Particle methods for Bayesian modeling and enhancement of speech signals. IEEE Trans. Speech and Audio Proc., vol. 10, no. 3, pp. 173-185, 2002.
• Vermaak J., Perez P., Gangnet M., and Blake A. Towards improved observation models for visual tracking: selective adaptation. Proc. Eur. Conf. on Computer Vision (ECCV), 2002.
• Viens F. Portfolio optimization under partially observed stochastic volatility. COMCON 8. The 8th International Conference on Advances in Communication and Control, W. Wells, Ed. 1-12. Optim. Soft., Inc, Pub. Div., 2002.
• Vlassis N., Terwijn B. and Krose B. Auxiliary particle filter robot localization from high-dimensional sensor observations. Proc. IEEE Int. Conf. on Robotics and Automation, pp. 7-12, Washington D.C., May 2002.
• Wolf J., Burgard W., Burkhardt H.. Using an Image Retrieval System for Vision-based Mobile Robot Localization. Proc. of the International Conference on Image and Video Retrieval (CIVR), 2002.
• Yao K., Nakamura S. Sequential noise compensation by sequential Monte Carlo method. Advances in Neural Information Processing Systems 14, edited by Thomas G. Dietterich, Sue Becker, and Zoubin Ghahramani, MIT press, 2002.
• Zhang J. L. and Liu J. S. A New Sequential Importance Sampling Method with Its Application to the 2D Hydrophobic-hydrophilic Model. Journal of Chemical Physics, vol. 117, pp. 3492-98, 2002.
• Zhao F., Shin J. and Reich J.. Information-Driven Dynamic Sensor Collaboration for Tracking Applications. IEEE Signal Processing Magazine, 19(2):61-72, March 2002.

2001

• Azimi-Sadjadi B. and Krishnaprasad P. S. Integer Ambiguity Resolution in GPS using Particle Filtering. Proceedings of 2001 American Control Conference, 2001.
• Barndorff-Nielsen O.E. and Shephard N. Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics (with discussion). Journal of the Royal Statistical Society, Series B, 63, pp. 167--241, 2001.
• Bølviken E., Storvik G., Glockner F. Deterministic and stochastic particle filters in state space models. In "Sequential Monte Carlo Methods in Practice," (eds: A. Doucet et al), 2001.
• Braathen B., Bartlett M.S. and Movellan J.R. 3-D head pose estimation from video by stochastic particle filtering. Proceedings of the 8th Annual Joint Symposium on Neural Computation, 2001.
• Choo K. and Fleet D.J. People tracking with hybrid Monte Carlo. IEEE International Conference on Computer Vision, Vancouver, Vol II, pp. 321-328 2001.
• Chu M., Haussecker H., Zhao F.. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. Int'l J. High Performance Computing Applications, 16(3):90-110, Fall 2002. Also, Xerox Palo Alto Research Center Technical Report P2001-10113, May 2001.
• Cemgil A.T. and Kappen B. Rhythm Quantization and Tempo Tracking by Sequential Monte Carlo. Advances in NIPS, 2001.
• Del Moral P., Miclo L. Particle approximations of Lyapunov exponents connected to Schr/"odinger operators and Feynman-Kac semigroups. Publications du Laboratoire de Statistiques et Probabilites, Toulouse III, 2001.
• Doucet A., Gordon N. J. and Krishnamurthy V. Particle Filters for State Estimation of Jump Markov Linear Systems. IEEE Trans. Signal Processing, vol. 49, no.3, pp. 613-624, 2001.
• Duraiswami R., Zotkin D., Davis L. Multimodal 3-D Tracking and Event Detection via the Particle Filter. Workshop on Event Detection in Video, International Conference on Computer Vision, Vancouver, British Columbia, 2001.
• Fong, W., Godsill, S.J., Doucet, A., and West, M., Monte Carlo smoothing with application to speech enhancement, IEEE Tr. Signal Processing, 2001.
• Fox D., Thrun S., Burgard W., and Dellaert F. Particle filters for mobile robot localization. In "Sequential Monte Carlo Methods in Practice," (eds: A. Doucet et al), 2001.
• Gadeyne K. and Lefebvre T. The application of probabilistic techniques for the state/parameter estimation of (dynamical) systems and pattern recognition problems. Internal report 2001R031, K.U.Leuven, Celestijnenlaan 300B, B-3001 Heverlee, Belgium, 2001. Also available in ps.gz.
• Godsill, S.J., and Clapp, T.C., Improvement strategies for Monte Carlo particle filters, In A Doucet, J. F. G. De Freitas, and N. J. Gordon, editors, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
• Godsill S. J., Doucet A., and West M. Maximum a posteriori sequence estimation using Monte Carlo particle filters. Ann. Inst. Stat. Math., 53(1):82-96, 2001.
• Haan, N.M., and Godsill, S.J., A time-varying model for DNA sequencing data submerged in correlated noise, In Proc. IEEE Workshop on Statistical Signal Processing, August 2001.
• Haan, N.M., and Godsill, S.J., Sequential methods for DNA sequencing, In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2001.
• Higuchi T. Evolutionary Time Series Model with Parallel Computing. The Third JAPAN-US Joint Seminar on Statistical Time Series Analysis, 2001.
• Higuchi T. Self-organizing Time Series Model. In Sequential Monte Carlo Methods in Practice, (eds. A. Doucet, J.F.G, de Freitas, and N.J.Gordon), pp.429-444, Springer-Verlag New York, 2001. Also available in ps.
• Hue C., Le Cadre J.-P., Perez P. A particle filter to track multiple objects. IEEE Workshop on Multi-Object Tracking, pp. 61-68, Vancouver, Canada, 2001.
• Hue C., Le Cadre J.-P., Perez P. The (MR)MTPF:particle filters to track multiple targets using multiple receivers. 4th International Conference on Information Fusion, FUSION'01, Montreal, Canada, 2001.
• Hue C., Le Cadre J.-P., Perez P. Tracking multiple objects with particle filtering using multiple receivers. International Seminar on "Target Tracking: Algorithms & Applications", pp. 61-64, Twente, Pays-Bas, 2001.
• Iba Y. Population Monte Carlo algorithms. Transactions of the Japanese Society for Artificial Intelligence , Vol.16 No.2, pp.279-286, 2001. Also preprint.
• Ikoma N., Ichimura N., Higuchi T., and Maeda H. Maneuvering target tracking by using particle filter. Joint 9th IFSA World Congress and 20th NAFIPS International Conference, the International Fuzzy System Association and the North American Fuzzy Information Processing Society, Vancouver, Canada, Jul.25-28, 2001.
• Ikoma N., Ichimura N., Higuchi T., and Maeda H. Particle filter based method for maneuvering target tracking. IEEE International Workshop on Intelligent Signal Processing, Budapest, Hungary, May 24-25, 2001.
• Karlsson R. and Gustafsson F. Monte Carlo data association for multiple target tracking. to IEE Workshop on Target Tracking, Eindhoven, NL, 2001. Available also in PS
• Koller-Meier E. B. and Ade F. Tracking Multiple Objects Using the Condensation Algorithm. Journal of Robotics and Autonomous Systems, Vol. 34, pp. 93-105, 2001.
• Koller-Meier E.B., Van Gool L. Modeling and Recognition of Human Actions Using a Stochastic Approach . 2nd European Workshop on Advanced Video-based Surveillance Systems AVBS'01, pp. 17-28, London, UK, 4th September 2001.
• Kwok C., Fox D. and Meila M. KLD-Sampling: Adaptive Particle Filters. NIPS, 2001.
• LeGland F. and Oudjane N. Stability and uniform approximation of nonlinear filters using the Hilbert metric, and application to particle filters. Research report RR-4215, INRIA. June, 2001. Abstract.
• Liu J. and West M. Combined parameter and state estimation in simulation-based filtering. In "Sequential Monte Carlo Methods in Practice," (eds: A. Doucet et al), 2001.
• Ormoneit D., Lemieux C. and Fleet D. Lattice Particle Filters. UAI, 2001.
• Ormoneit D., Sidenbladh H., Black M. J. and Hastie T. Learning and tracking cyclic human motion. Advances in Neural Information Processing Systems 13, pp 894-900, 2001.
• Perez P., Blake A. and Gangnet M. JetStream: Probabilistic contour extraction with particles. Proc. Int. Conf. on Computer Vision (ICCV), II:524-531, 2001.
• Poupart P., Ortiz L.E. and Boutilier C. Value-Directed Sampling Methods for Monitoring POMDPs. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 453-461, Seattle, 2001. Also available in ps and ps.gz.
• Rui Y. and Chen Y. 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.
• Schulz D. and Burgard W. Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems, 34 (2-3), 2001.
• Schulz D., Burgard W., Fox D., and Cremers A. B. Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data Association. Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 2001.
• Sidenbladh H. and Black M. J. Learning image statistics for Bayesian tracking. IEEE International Conference on Computer Vision, vol 2, pp 709-716, Vancouver, Canada 2001.
• Sullivan J., Blake A., Isard M. and MacCormick J. Bayesian Object Localisation in Images. Int. J. Computer Vision, 44, 2, 111-136, 2001.
• Thrun S. A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5):335-363, 2001.
• Thrun S., Langford J., and Verma V. Risk sensitive particle filters. Advances in Neural Information Processing Systems 14, MIT Press, 2001.
• Torma P. and Szepesvari Cs. Efficient Object Tracking in Video Sequences by means of LS-N-IPS . Proc. Second International Symposium on Image and Signal Processing and Analysis (ISAP'01), pp. 277-282, 2001.
• Torma P. and Szepesvari Cs. LS-N-IPS: an Improvement of Particle Filters by Means of Local Search . NOLCOS, 2001.
• Vermaak J., Blake A., Gangnet M., and Perez P. Sequential Monte Carlo fusion of sound and vision for speaker tracking. Proc. Int. Conf. on Computer Vision (ICCV), I:741-746, 2001.
• Zotkin D., Duraiswami R., Nanda H. and Davis L. Multimodal Tracking for Smart Videoconferencing. Second Intenational Conference on Multimedia and Expo, Tokyo, Japan, August 2001.

2000

• Azimi-Sadjadi B. and Krishnaprasad P. S. Approximate Nonlinear Filtering and Its Applications for GPS. Institute for Systems Research Technical Report, TR 2000-37, September 2000.
• Azimi-Sadjadi B. and Krishnaprasad P. S. Approximate Nonlinear Filtering and Its Applications for GPS. Proceedings of 39th IEEE Conference on Decision and Control, 1579-84, Sydney, Australia, Dec. 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, 2000.
• Chen Y. and Liu J.S. Discussion on ``Inference in molecular population genetics'' by M. Stephens and P. Donnelly. Journal of the Royal Statistical Society Series B, vol. 62, pp. 644-645, 2000.
• Cerou F. and LeGland F. Efficient Particle Methods for Residual Generation in Partially Observed SDE's. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, December 12-15, pp. 1200-1205 2000.
• Davis L., Philomin V. and Duraiswami R. Tracking people in outdoor environments. 15th International Conference on Pattern Recognition, , Barcelona, Spain, 2000.
• Del Moral P., Ledoux M. On the Convergence and the Applications of Empirical Processes for Interacting Particle Systems and Nonlinear Filtering. Journal of Theoret. Probability Vol. 13, No. 1, 225-257, 2000.
• Deutscher J., Blake A. and Reid I. Articulated Body Motion Capture by Annealed Particle Filtering. Proc. Conf. Computer Vision and Pattern Recognition (CVPR), 2000.
• Doucet A., de Freitas N., Murphy K. and Russell S. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Uncertainty in Artificial Intelligence (UAI) 2000.
• Doucet A., Godsill S. J., and Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statist. Comp., 10:197-208, 2000.
• Doucet A., Godsill S. J., and Andrieu C. Monte Carlo filtering and smoothing with application to time-varying spectral estimation. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. II, pp. 701-704, 2000.
• Godsill S. J., Doucet A., and West M. Methodology for Monte Carlo smoothing with application to time-varying autoregressions. Symposium on Frontiers of Time Series Modelling, Institute of Statistical Mathematics, Tokyo, 2000.
• Grassberger P., Nadler W. "Go with the winners"-Simulations. Cond-mat/0010265. Proceedings der Heraeus-Ferienschule "`Vom Billiardtisch bis Monte Carlo: Spielfelder der statistischen Physik"', Chemnitz, Oktober 2000; cond-mat/0010265.
• Higuchi T., Kitagawa G. Knowledge Discovery and Self-Organizing State Space Model. IEICE Transactions on Information and Systems, E83-D, No.1, 36-43, 2000. Also available in ps.
• Højen-Sørensen P., de Freitas N. and Fog T. On-Line Probabilistic Classification with Particle Filters. IEEE International Workshop on Neural Networks for Signal Processing (NNSP2000), Sidney, Australia, 2000.
• Hue C., Le Cadre J.-P., Perez P. Tracking Multiple Objects with Particle Filtering. Rapport de recherche IRISA, No 1361, Octobre 2000.
• Ikoma N., Ichimura N. and Higuchi T. Maneuvering target tracking by nonlinear non-Gaussian state space model. Proc. of the 2nd International Symposium on Frontiers of Time Series Modeling, Nonparametric Approach to Knowledge Discovery, Nara, JAPAN, Dec.14-17, pp.236-237, 2000.
• Ikoma N. and Maeda H. Nonstationary spectral peak estimation by Monte Carlo Filter. Symposium 2000: Adaptive Systems for Signal Processing, Communications, and Control (AS-SPCC), IEEE, Alberta, Canada, Oct.1-4, pp.245-250, 2000.
• LeGland F. and Oudjane N. Stability and Approximation of Nonlinear Filters using the Hilbert Metric, and Application to Particle Filters. Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, December 12-15, pp. 1585-1590 2000.
• MacCormick J. and Isard M. Partitioned sampling, articulated objects, and interface-quality hand tracking. Proc European Conf. Computer Vision, vol. 2, pp. 3-19, 2000.
• Ormoneit D., Sidenbladh H. and Black M. J., Hastie T. and Fleet D.J. Learning and tracking human motion using functional analysis. IEEE Workshop on Human Modeling, Analysis and Synthesis, Hilton Head, SC, USA 2000.
• Philomin V., Duraiswami R. and Davis L. Quasi-Random Sampling for Condensation. Proceedings ECCV, 2000.
• Philomin V., Duraiswami R. and Davis L. Pedestrian Tracking from a Moving Vehicle. IEEE workshop on Intelligent Vehicles, Dearborn, MI, 2000.
• Punskaya E., Andrieu C., Doucet A. and Fitzgerald W. J. Particle Filtering for Optimal Detection in Fading Channels.Technical report CUED/F-INFENG/TR 384, Cambridge University Department of Engineering, 2000.
• Rittscher J., Kato J., Joga S. and Blake A. A Probabilistic Background Model for Tracking. Proc European Conf. Computer Vision, vol. 2, pp. 336-350, 2000.
• Sidenbladh H., Black M. J. and Fleet D. J. Stochastic tracking of 3D human figures using 2D image motion. European Conference on Computer Vision, vol 2, pp 702-718, Dublin, Ireland 2000.
• Srivastava A.. A Nonlinear Filtering Method for Geometric Subspace Tracking. IEEE Sensor Array and Multichannel Signal Processing Workshop, Boston March 2000.
• Srivastava A.. Bayesian Filtering for Tracking Pose and Location of Rigid Targets. Proceedings of SPIE Aerosense, Orlando, FL, April 2000.
• Thrun S. Monte carlo POMDPs. Advances in Neural Information Processing Systems 12, eds. S.A. Solla, T.K. Leen, and K.-R. Muller, pp. 1064-1070. MIT Press, 2000.
• Thrun S. Probabilistic algorithms in robotics. AI Magazine, 21(4):93-109, 2000.
• Thrun S., Fox D., and Burgard W. Monte carlo localization with mixture proposal distribution. Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX, 2000.
• Thrun S., Fox D., Burgard W., and Dellaert F. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 101:99-141, 2000.
• Van der Merwe R., Doucet A., de Freitas N. and Wan E. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000.

1999

• Andrieu C., de Freitas N., Doucet A. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks. Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, May 1999.
• Andrieu C., de Freitas N., Doucet A. Sequential MCMC for Bayesian Model Selection. IEEE Signal Processing Workshop on Higher Order Statistics. Ceasarea, Israel, June 14-16. 1999.
• Carpenter J., Clifford P. and Fearnhead P. An improved particle filter for non-linear problems. IEE proceedings - Radar, Sonar and Navigation, Vol. 146, pp. 2-7, 1999.
• Carpenter J., Clifford P. and Fearnhead P.. Building Robust Simulation-based Filters for Evolving Data Sets.
• Clapp T. C. and Godsill S. J. 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.
• Clapp T. C. and Godsill S. J. Fixed-lag smoothing using sequential importance sampling. In Bayesian Statistics VI, eds. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith. 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.
• Dellaert F., Fox D., Burgard W., and Thrun S. Monte Carlo Localization for Mobile Robots. IEEE International Conference on Robotics and Automation (ICRA99), May 1999. First paper on SMC in robot localization.
• Dellaert F., Burgard W., Fox D., and Thrun S. Using the condensation algorithm for robust, vision-based mobile robot localization. Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.
• Everson R. and Roberts S. J. Non-stationary Independent Component Analysis. Technical Report TR-99-1. March 1999. In Proceedings of ICANN-99, 503-508.
• Fox D., Burgard W., Dellaert F. and Thrun S. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. In Proceedings of AAAI-99.
• Kitagawa G. and Higuchi T. Automatic Transaction of Signal via Statistical Modeling. New Generation Computing, Vol.18,17-28,1999. Also available in ps.
• Lanterman A. 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.
• Meier E. B. and Ade F. Tracking Cars in Range Images Using the Condensation Algorithm. IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems ITSC'99 , pp. 129-134, Tokyo, Japan, 5th-8th October 1999.
• Meier E. B. and Ade F. Using the Condensation Algorithm to Implement Tracking for Mobile Robots. Third European Workshop on Advanced Mobile Robots EUROBOT'99, pp. 73-80, Zurich, Switzerland, 6th-8th September 1999.
• Pitt M. and Shephard N. Filtering via simulation: auxiliary particle filter. Journal of the American Statistical Association, Vol. 94, 590-9, 1999.
• Thrun S., Langford J., and Fox D. Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. In Proceedings of ICML-99.
• Vermaak J., Andrieu C., Doucet A. and Godsill S. J. On-line Bayesian modelling and enhancement of speech signals. Technical Report CUED/F-INFENG/TR.361, Cambridge University Engineering Department, Cambridge, England, 1999.

1998

• Fearnhead P. Sequential Monte Carlo methods in filter theory. PhD thesis, University of Oxford, 1998.
• de Freitas N., Niranjan M., Gee A. and Doucet A. Sequential Monte Carlo Methods for Optimisation of Neural Network Models. Technical report CUED/F-INFENG/TR 328, Cambridge University Department of Engineering, July 1998.
• Doucet A. On Sequential Simulation-Based Methods for Bayesian Filtering. Technical report CUED/F-INFENG/TR 310, Cambridge University Department of Engineering, 1998. Appeared in Statistics and Computing.
• Isard M. and Blake A. ICondensation: Unifying low-level and high-level tracking in a stochastic framework. Proc 5th European Conf. Computer Vision, Vol. 1 893-908, 1998.
• Isard M. and Blake A. A smoothing filter for Condensation. Proc 5th European Conf. Computer Vision, Vol. 1 767-781, 1998.
• Isard M. and Blake A. CONDENSATION - conditional density propagation for visual tracking. Int. J. Computer Vision, 29, 1, 5-28, 1998.
• Liu J. and Chen R. Sequential Monte Carlo Methods for Dynamic Systems. J. Amer. Statist. Assoc., Vol 93, 1032-1044, 1998.

• Isard, M. and Blake, A., Contour tracking by stochastic propagation of conditional density, In Proc. European Conf. Computer Vision, 1996.
• West M. Mixture models, Monte Carlo, Bayesian updating and dynamic models.. In "Computing Science and Statistics," Vol 24, 325-333, 1993.