R. Albert and A. Barabsi, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.74, issue.1, pp.47-97, 2002.
DOI : 10.1103/RevModPhys.74.47

S. Ali and M. Shah, Floor Fields for Tracking in High Density Crowd Scenes, Computer Vision ECCV, pp.1-14, 2008.
DOI : 10.1007/978-3-540-88688-4_1

D. Angelova and L. Mihaylova, Extended Object Tracking Using Monte Carlo Methods, IEEE Transactions on Signal Processing, vol.56, issue.2, pp.825-832, 2008.
DOI : 10.1109/TSP.2007.907851

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

C. Berzuini, G. Nicola, W. R. Gilks, and C. Larizza, Dynamic Conditional Independence Models and Markov Chain Monte Carlo Methods, Journal of the American Statistical Association, vol.24, issue.440, pp.921403-1412, 1997.
DOI : 10.1080/01621459.1997.10473661

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

A. Carmi, F. Septier, and S. J. , The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking, 12th International Conference on Information Fusion, pp.1179-1186, 2007.
DOI : 10.1016/j.automatica.2012.06.086

URL : https://hal.archives-ouvertes.fr/hal-00566634

J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu, Learning Bayesian networks from data: An information-theory based approach, Artificial Intelligence, vol.137, issue.1-2, pp.1-243, 2002.
DOI : 10.1016/S0004-3702(02)00191-1

H. Geffner, Default Reasoning: Causal and Conditional Theories, 1992.

A. Gning, L. Mihaylova, S. Maskell, S. K. Pang, and S. Godsill, Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods, IEEE Transactions on Signal Processing, vol.59, issue.4, pp.523-536, 2011.
DOI : 10.1109/TSP.2010.2103062

C. W. Granger, Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Econometrica, vol.37, issue.1 4, pp.424-438, 1969.
DOI : 10.1017/CBO9780511753978.002

P. J. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

D. Helbing, Traffic and related self-driven many-particle systems, Reviews of Modern Physics, vol.73, issue.4, pp.1067-1141, 2002.
DOI : 10.1103/RevModPhys.73.1067

URL : http://arxiv.org/abs/cond-mat/0012229

P. W. Holland, Statistics and Causal Inference, Journal of the American Statistical Association, vol.10, issue.396, pp.945-960, 1986.
DOI : 10.1080/01621459.1980.10477517

Z. Khan, T. Balch, and F. Dellaert, MCMC-based particle filtering for tracking a variable number of interacting targets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.11, pp.1805-1819, 2002.
DOI : 10.1109/TPAMI.2005.223

R. Mahler, Statistical Multisource-multitarget Information Fusion, p.3, 2007.
DOI : 10.1201/9781420053098.ch16

L. Mihaylova, R. Boel, and A. Hegyi, Freeway traffic estimation within particle filtering framework, Automatica, vol.43, issue.2, pp.290-300, 2002.
DOI : 10.1016/j.automatica.2006.08.023

S. K. Pang, J. Li, and S. J. , Detection and Tracking of Coordinated Groups, IEEE Transactions on Aerospace and Electronic Systems, vol.47, issue.1, pp.472-502, 2007.
DOI : 10.1109/TAES.2011.5705687

J. Pearl, Causality: Models, Reasoning, and Inference, 2000.
DOI : 10.1017/CBO9780511803161

C. W. Reynolds, Flocks, herds and schools: A distributed behavioral model, ACM SIGGRAPH Computer Graphics, vol.21, issue.4, pp.25-34, 1987.
DOI : 10.1145/37402.37406

Y. Shoam, Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence, 1988.

B. Vo, S. Singh, and A. Doucet, Sequential Monte Carlo Methods for Multi-Target Filtering with Random Finite Sets, IEEE Transactions on Aerospace and Electronic Systems, vol.41, issue.4, pp.1224-1245, 2002.