Time varying dynamic Bayesian network for nonstationary events modeling and online inference

IEEE Transactions on Signal Processing

Volumn 59, Issue 4, 2011, Pages 1553-1568

  Wang, Zhaowen ^a   Kuruoǧlu, Ercan E ^b   Yang, Xiaokang ^c   Xu, Yi ^c   Huang, Thomas S ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b ISTI CNR   (Italy)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Bayesian networks; event recognition; particle filters; time varying

Indexed keywords

BAYESIAN FRAMEWORKS; DATA SEQUENCES; DATA VALUES; EVENT RECOGNITION; GENERAL MODEL; JOINT SPACE; MULTINOMIALS; NETWORK STATE; NETWORK STRUCTURES; NON-STATIONARY MODEL; NONSTATIONARY; NUMERICAL PARAMETERS; ON-LINE NETWORK; PARTICLE FILTERING; PARTICLE FILTERS; SYNTHETIC DATA; TIME VARYING; TIME-VARYING DYNAMICS; TIME-VARYING MODELS; VIDEO SEQUENCES;

DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; KNOWLEDGE BASED SYSTEMS; NONLINEAR FILTERING; RANDOM PROCESSES; SPEECH RECOGNITION; TIME VARYING NETWORKS;

BAYESIAN NETWORKS;

EID: 79952665170     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2010.2103071     Document Type: Article

References (44)

1. K. Murphy, "Dynamic Bayesian networks: Representation, inference and learning," Ph.D. dissertation, Comput. Sci. Div., Univ. Calif., Berkeley, Jul. 2002.
2. R. Prado, G. Huerta, and M. West, "Bayesian time-varying autoregressions: Theory, methods and applications," Resenhas, vol. 4, pp. 405-422, 2001.
3. E. Moulines, P. Priouret, and F. Roueff, "On recursive estimation for time varying autoregressive processes," Ann. Stat., vol. 33, no. 6, pp. 2610-2654, 2005. (Pubitemid 43384870)
4. L. D. Johnson and G. Sakoulis, "Maximizing equity market sector predictability in a bayesian time-varying parameter model," Computat. Stat. Data Anal., vol. 52, no. 6, pp. 3083-3106, 2008. (Pubitemid 351241288)
5. A. Rao, D. J. States, and J. D. Engel, "Inferring time-varying network topologies from gene expression data," EURASIP J. Bioinformat. Syst. Biol., vol. 2007, p. 12, 2007.
6. M. West, R. Prado, and A. D. Krystal, "Evaluation and comparison of EEG traces: Latent structure in nonstationary time series," J. Amer. Statist. Assoc., vol. 94, no. 446, pp. 375-387, 1999.
7. D. Gencaga, E. E. Kuruoglu, and A. Ertuzun, "Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering," Multidimens. Syst. Signal Process., vol. 21, no. 1, pp. 73-85, 2010.
8. A. Ihler, J. Hutchins, and P. Smyth, "Adaptive event detection with time-varying poisson processes," in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., Aug. 2006, pp. 207-216. (Pubitemid 44535517)
9. Z. Ghahramani and G. E. Hinton, "Variational learning for switching state-space models," Neural Comput., vol. 12, no. 4, pp. 831-864, Apr. 1, 2000.
10. A. Blake, B. North, and M. Isard, "Learning multi-class dynamics," Neural Inf. Process. Syst. (NIPS), pp. 389-395, 1998.
11. V. Pavlovic, J. M. Rehg, and J. Maccormick, "Learning switching linear models of human motion," in Proc. Neural Inf. Process. Syst. (NIPS), 2000, pp. 981-987.
12. S. M. Oh, J. M. Rehg, T. Balch, and F. Dellaert, "Learning and inferring motion patterns using parametric segmental switching linear dynamic systems," Int. J. Comput. Vis., vol. 77, pp. 103-124, 2008. (Pubitemid 351294739)
13. E. Fox, E. Sudderth, M. Jordan, and A. Willsky, "Nonparametric bayesian learning of switching linear dynamical systems," Adv. Neural Inf. Process. Syst., vol. 21, pp. 457-464, 2009.
14. P. Turaga and R. Chellappa, "Locally time-invariant models of human activities using trajectories on the grassmannian," in Proc. IEEE Conf. Comput. Vision Pattern Recogn., 2009, pp. 2435-2441.
15. C. Andrieu, M. Davy, and A. Doucet, "Efficient particle filtering for jump Markov systems. application to time-varying autoregressions," IEEE Trans. Signal Process., vol. 51, no. 7, pp. 1762-1770, Jul. 2003.
16. X. Xuan and K. Murphy, "Modeling changing dependency structure in multivariate time series," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 1055-1062.
17. P. Fearnhead, "Exact and efficient Bayesian inference for multiple changepoint problems," Stat. Comput., vol. 16, no. 2, pp. 203-213, Jun 2006. (Pubitemid 43742297)
18. K.Wang, J. Zhang, F. Shen, and L. Shi, "Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series," Knowl. Inf. Syst., vol. 17, no. 1, pp. 121-133, 2008.
19. J. W. Robinson and A. J. Hartemink, "Non-stationary dynamic bayesian networks," in Neural Inf. Process. Syst. (NIPS), 2008, pp. 1369-1376.
20. M. Grzegorczyk and D. Husmeier, "Non-stationary continuous dynamic Bayesian networks," Adv. Neural Inf. Process. Syst. 22 (NIPS), pp. 682-690, 2009.
21. E. Punskaya, C. Andrieu, A. Doucet, and W. Fitzgerald, "Bayesian curve fitting using MCMC with applications to signal segmentation," IEEE Trans. Signal Process., vol. 50, no. 3, pp. 747-758, Mar. 2002. (Pubitemid 34295114)
22. S. H. Nielsen and T. D. Nielsen, "Adapting Bayes network structures to non-stationary domains," Int. J. Approx. Reason., vol. 49, no. 2, pp. 379-397, 2008.
23. A. Tucker and X. Liu, "A bayesian network approach to explaining time series with changing structure," Intell. Data Anal., vol. 8, no. 5, pp. 469-480, 2004.
24. F. Guo, S. Hanneke, W. Fu, and E. P. Xing, "Recovering temporally rewiring networks: A model-based approach," in Proc. 24th Int. Conf. Mach. Learning, 2007, pp. 321-328.
25. L. Song, M. Kolar, and E. P. Xing, "Time-varying dynamic Bayesian networks," in Proc. 23rd Neural Inf. Process. Syst. (NIPS), 2009.
26. M. Kolar, L. Song, and E. P. Xing, "Sparsistent learning of varyingcoefficient models with structural changes," Adv. Neural Inf. Process. Syst., vol. 22, pp. 1006-1014, 2009.
27. M. Kolar, L. Song, A. Ahmed, and E. P. Xing, "Estimating time-varying networks," Ann. Appl. Stat., vol. 4, pp. 94-123, 2010.
28. W. Fu, L. Song, and E. P. Xing, "Dynamic mixed membership block model for evolving networks," in Proc. 26th Int. Conf. Mach. Learn., 2009.
29. D. Heckerman, "A Tutorial on Learning with Bayesian Networks," in Learning in Graphical Models,M. Jordan, Ed. Cambridge, MA:MIT Press, 1999.
30. N. Friedman, K. Murphy, and S. Russell, "Learning the structure of dynamic probabilistic networks," in Proc. 14th Ann. Conf. Uncert. Artif. Intell., 1998, pp. 139-214.
31. A.-L. Barabási and R. Albert, "Emergence of scaling in random networks," Science, vol. 286, pp. 509-512, 1999.
32. R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and E. Upfal, "Stochastic models for the web graph," in Proc. IEEE 41st Ann. Symp. Found. Comput. Sci., 2000, pp. 57-65. (Pubitemid 32075878)
33. N. Deo and A. Cami, "Preferential deletion in dynamic models of weblike networks," Inf. Process. Lett., vol. 102, pp. 156-162, 2007. (Pubitemid 46399703)
34. P. J. Green, "Reversible jump Markov chain Monte Carlo computation and Bbayesian model determination," Biometrica, vol. 82, no. 4, pp. 711-732, Dec. 1995.
35. G. Schwarz, "Estimating the dimension of a model," Ann. Statist., vol. 6, no. 2, pp. 461-464, 1978.
36. S. Veeramachaneni, D. Sona, and P. Avesani, "Hierarchical Dirichlet model for document classification," in Proc. Int. Conf. Mach. Learn., Bonn, Germany, 2005, vol. 119, pp. 928-935.
37. D. Geiger and D. Heckerman, "Learning Gaussian networks," in Proc. 10th Ann. Conf. Uncert. Artif. Intell., 1994, pp. 235-243.
38. R. D. Shachter and C. R. Kenley, "Gaussian influence diagrams," MNSC, vol. 35, no. 5, pp. 527-550, 1989.
39. A. Doucet, S. Godsill, and C. Andrieu, "On sequential monte carlo sampling methods for Bayesian filtering," Stat. Comput., vol. 10, pp. 197-208, 2000.
40. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-189, Feb. 2002.
41. P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, "Color-based probabilistic tracking," in Proc. 7th Eur. Conf. Comput. Vision-Part I, 2002, pp. 661-675.
42. R. Collins, X. Zhou, and S. K. Teh, "An open source tracking testbed and evaluation web site," in Proc. IEEE Int. Workshop Perform. Evaluation Tracking Surveill. (PETS 2005), Jan. 2005.
43. A. Dore and C. Regazzoni, "Interaction analysis with a Bayesian trajectory model," IEEE Intell. Syst., vol. 25, no. 3, pp. 32-40, May/Jun. 2010.
44. "Context aware vision using image-based active recognition project," EC Funded Project/IST 2001 37540 2005, CAVIAR, [Online]. Available: