Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks

  • Eva Besada-Portas
  • Sergey M. Plis
  • Jesus M. de la Cruz
  • Terran Lane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)


Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the number of particles grows exponentially with the dimensionality of the hidden state space. The problem is aggravated when the initial distribution of the variables is not well known, as happens in global localization problems. We present a new parallel PF for systems whose variable dependencies can be factored into a Dynamic Bayesian Network. The new algorithms significantly reduce the number of particles, while independently exploring different subspaces of hidden variables to build particles consistent with past history and measurements. We demonstrate this new PF approach on some complex dynamical system estimation problems, showing that our method successfully localizes and tracks hidden states in cases where traditional PFs fail.


Root Mean Square Error Particle Filter Time Slice Blood Oxygenation Level Dependent Importance Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Doucet, A., Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo methods in practice. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  2. 2.
    MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: 16th Conference on Uncertainty in Artificial Intelligence, pp. 176–183 (2000)Google Scholar
  4. 4.
    Vaswani, N.: Particle filters for infinite (or large) dimensional state spaces-part 2. In: IEEE ICASSP (2006)Google Scholar
  5. 5.
    Ng, B., Peshkin, L.: Factored particles for scalable monitoring. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, pp. 370–377. Morgan Kaufmann, San Francisco (2002)Google Scholar
  6. 6.
    Das, S., Lawless, D., Ng, B., Pfeffer, A.: Factored particle filtering for data fusion and situation assessments in urban environments. In: 8th International Conference on Information Fusion (July 2005)Google Scholar
  7. 7.
    Park, S., Hwang, J., Rou, K., Kim, E.: A new particle filter inspired by biological evolution: Genetic Filter. Proceeding of World Academy of Science, Engineering and Technology 21, 57–71 (2007)Google Scholar
  8. 8.
    Brandao, B.C., Wainer, J., Goldenstein, S.K.: Subspace hierarchical particle filter. In: XIX Brazilian Symposium on Computer Graphics and Image Processing (2006)Google Scholar
  9. 9.
    Koller, D., Lerner, U.: Sampling in factored dynamic systems. In: Sequential Monte Carlo in Practice, pp. 445–464 (2001)Google Scholar
  10. 10.
    Maccormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. Intenational Journal of Computer Vision 39(1), 57–71 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Klaas, M., de Freitas, N., Doucet, A.: Toward practical n2 Monte Carlo: the Marginal Particle Filter. In: Proceedings of UAI 2005, Arlington, Virginia, pp. 308–331. AUAI Press (2005)Google Scholar
  12. 12.
    Rose, C., Saboune, J., Charpillet, F.: Reducing particle filtering complexity for 3D motion capture using dynamic bayesian networks. In: 23th AAAI Conference on Artificial Intelligence (2008)Google Scholar
  13. 13.
    Pitt, M.K., Shephard, N.: Filtering via simulation: Auxiliary Particle Filters. Journal of the American Statistical Association 94(446), 590–599 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P.P.: Discrete dynamic bayesian network analysis of fMRI data. Human Brain Mapping (November 2007)Google Scholar
  15. 15.
    Friston, K.J., Harrison, L., Penny, W.: Dynamic Causal Modelling. NeuroImage 19(4), 1273–1302 (2003)CrossRefGoogle Scholar
  16. 16.
    Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping 10(3), 120–131 (2000)CrossRefGoogle Scholar
  17. 17.
    Hofmann, R., Tresp, V.: Discovering structure in continuous variables using bayesian networks. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 500–506. MIT Press, Cambridge (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Eva Besada-Portas
    • 1
  • Sergey M. Plis
    • 1
  • Jesus M. de la Cruz
    • 2
  • Terran Lane
    • 1
  1. 1.University of New MexicoAlbuquerqueUSA
  2. 2.Universidad Complutense de MadridMadridSpain

Personalised recommendations