Sequential Monte Carlo Methods

LATEST ADDITIONS

• Flury, T. and Shephard, N. Learning and filtering via simulation: smoothly jittered particle filters. Preprint. 2009
  

• Fearnhead, P. and Liu, Z., Efficient Bayesian Analysis of Multiple Changepoint  Models with Dependence across Segments. Preprint, 2009.
  

• Khalaf -Allah, M. Nonparametric Bayesian Filtering for Location Estiatinon, Position Tracking and Global Localization of Mobile Terminals in Outdoor Wireless Environments. EURASIP Journal on Advances in Signal Processing, 2008.
  
• Ikonen, E. , Gomez-Ramirez, E. and Najim, K. Process Regulation via Genealogical Decision Trees. Optimal Control Applications and Methods. Volume 30, Issue 2. 2009.
• Krach, B. and Weigel, R. Markovian Channel Modelling for Multipath Mitigation in Navigation Receivers. Preprint 2009.
  

• Del Moral, P. , Doucet, A., and Jasra, A. An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation. Preprint 2008.
  
• Givon, D., Stinis, P. and Weare, J. Variance Reduction for Particle Filters of Systems with Time Scale Separation. IEEE Transactions on Signal Processing. 57(2), 2009.
  
• Nevat, I., Peters, G. and Yuan, J. Coherent Detection for Cooperative Networks with Arbitrary Relay Functions using Likelihood--free Inference. Preprint 2009.
• Peters, G. Wuthrich, M. and Shevchenko, P. Chain Ladder Method: Bayesian Bootstrap versus Classical Bootstrap. Preprint 2009.

• Krach, B.  and Robertson P. Cascaded Estimation Architecture for Integration of Foot-Mounted Inertial Sensors. Preprint 2008.
  
• Krach, B., Lentmaier, M.  and Robertson P. Joint Bayesian Positioning  and Multipath Mitigation in GNSS. Preprint 2008.
  
• Lentmaier, M. Krach, B. and Robertson P. Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments. Journal of Navigation and Observation. 2008.
  
• Myung, J. I. and Pitt, M. A. Optimal  Experimental Design for Model Discrimination. Preprint 2008.
  

• Jasra, A. Sequential Monte Carlo Methods for Option Pricing. Preprint, 2008.
  

• Del Moral, P.,  Doucet, A., and Jasra, A. On adaptive resampling procedures for sequential Monte Carlo methods. Preprint 2008.
• Jasra, A. and Holmes, C. Stochastic Boosting Algorithms. Preprint, 2008.
  

• Jasra, A., Stephens, D., Doucet, A. and Tsagaris, T. Inference for Levy driven Stochastic Volatility Models via Adaptive SMC. Preprint, 2008.
• Jasra, A. and Doucet, A. Optimal Control of a Class of Nonlinear Diffusions. Preprint, 2008.
  
• Särkkä, S. and Sottinen, T. Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-time Non-linear Systems. Bayesian Analysis, Volume 3, Number 3 pp 555-584. 2008.
  

• Johansen, A. M., SMCTC: Sequential Monte Carlo in C++. Research report 08:16, University of Bristol, Department of Mathematics - Statistics Group, July 2008.
  

• 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.
  
• Fearnhead, P. Wyncoll, D. and Tawn, J. A Sequential Smoothing Algorithm with Linear Computational Cost. Preprint. May 2008.
• Peters, G.W., Fan, Y., and Sisson, S.A. On Sequential Monte Carlo, Rejection Control and Approximate Bayesian Computation. Preprint, 2008.
  
• Ristic, B.,  Arulampalam, S. and Gordon, N. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. Boston, 2004.
• 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.
  

• Lombardi, M. and Sgherri, S. (Un)naturally Low? Sequential Monte Carlo Tracking of the US Natural Interest Rate. ECB Working Paper No. 794.
  

• Closas, P., Fernández-Prades, C. and Fernández-Rubio, J.A. A Particle Filtering Tracking Algorithm for GNSS Synchronization using Laplace's method. To Appear in Proceedings of IEEE ICASSP 2008.
  
• Najim, K., Ikonen, E. and Gomez-Ramirez, E. Trajectory tracking control based on a genealogical decision tree controller for robot manipulators. International Journal of Innovative Computing and Control. Vol. 4. No. 1. January, 2008, p. 53-62.
  

• 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. A Note on the Auxiliary Particle Filter. Statistics and Probability Letters. To appear. 2008.
• Karlsson, R., Schön, T.B., Törnqvist, D., Conte, G. and Gustafsson, F. Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application. Proceedings of the IEEE Aerospace Conference, Big sky, MT, USA, Marcho 2008.
• Vaswani, N. Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods. Accepted IEEE Transactions on Signal Processing.
  
• Vaswani, N., Yezzi, A.,  Rathi, Y. and Tannenbaum, A. Time-varying Finite Dimensional Basis for Tracking Contour Deformations. Proceedings of IEEE Conference on Decision and Control (CDC), 2006.
  
• Wang, Y-D., Wu, J-K. and Kassim, A. Adaptive Particle Filter for Data Fusion of Multiple Cameras. Journal of VLSI Signal Processing Systems. Volume 49, Number 3. December 2007.
• Wills, A.G., Schön, T.B. and Ninness, B. Parameter Estimation for Discrete-Time Nonlinear Systems Using EM. Proceedings of the 17th IFAC World Congress, Seoul, South Korea, July 2008.

• Fearnhead, P. Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. To Appear in Statistics and Computing.
• 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, Volume 6567, 65670S, 2007. PFlib is released under  a GNU GPL license. To get a copy email  Lingji.Chen@ ssci.com.
  

• 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.
  
• Fernández-Prades, C., Closas, P., 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.
  
• 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.
  
• 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). p. 1-30. Fall 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.
  

• Johansen, A.M. and Doucet, A., Auxiliary variable sequential Monte Carlo methods, University of Bristol Technical Report #07-09, 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.
  

• Fearnhead, P. Computational Methods for Complex Stochastic Systems: Alternatives to MCMC. Preprint. 2007.
• 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.
• 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.
  
• Whiteley, N., Singh, S. and Godsill, S. Auxiliary Particle Implementation of the Probability Hypothesis Density Filter. To appear in Proceedings of IEEE ISPA 2007.
  

• 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.
  
• 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.
  
• Peters, G. Topics in Sequential Monte Carlo Samplers. M.Sc. Thesis, University of Cambridge, UK.
  
• 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.

• 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. 

• Fearnhead, P. and Liu, Z., Online Inference for Multiple Change Points Problems. To appear in JRSS Series B.
• Hoey, J., Tracking Using Flocks of Features, with Application to Assisted Handwashing. In Proceedings of British Machine Vision Conference (BMVC) 2006, Edinburgh, Scotland.
• Singh, J., Kumar, R., Madhow, U. , Suri,S.,  Cagley, R.,  Tracking Multiple Targets Using Binary Proximity Sensors. Information Processing in Sensor Netorks (IPSN07), 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.
• 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.
• Sisson S. A., Y. Fan and M. M. Tanaka , Sequential Monte Carlo without Likelihoods. Proc. Natl. Acad. Sci. USA. In press.