D. Abrial, D. Calavas, N. Jarrige, and C. Ducrot, Poultry, pig and the risk of BSE following the feed ban in France ??? A spatial analysis, Veterinary Research, vol.36, issue.4, p.615628, 2005.
DOI : 10.1051/vetres:2005020

URL : https://hal.archives-ouvertes.fr/hal-00902988

M. Alfo, L. Nieddu, and D. Vicari, Finite Mixture Models for Mapping Spatially Dependent Disease Counts, Biometrical Journal, vol.14, issue.6, p.8497, 2009.
DOI : 10.1002/bimj.200810494

A. Allepuz, A. Lopez-quilez, A. Forte, G. Fernandez, and J. Casal, Spatial analysis of bovine spongiform encephalopathy in Galicia Preventive Veterinary Medicine, pp.2-4174185, 2002.

J. Ryan, L. Hoinville, J. Hillerton, and A. Andstin, Transmission dynamics and epidemiology of BSE in British cattle, Nature, 1996.

J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, p.259302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

J. Besag, J. York, and A. Mollie, Bayesian image restoration, with two applications in spatial statistics, Annals of the Institute of Statistical Mathematics, vol.74, issue.1, p.159, 1991.
DOI : 10.1007/BF00116466

C. Biernacki, Initializing EM using the properties of its trajectories in Gaussian mixtures, Statistics and Computing, vol.14, issue.3, p.267279, 2004.
DOI : 10.1023/B:STCO.0000035306.77434.31

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, p.561575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

J. Blanchet and F. Forbes, Triplet Markov elds for the supervised classication of complex structure data, IEEE Trans. on Pattern Analyis and Machine Intelligence, vol.30, issue.6, p.10551067, 2008.

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean eldlike approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, p.131144, 2003.
DOI : 10.1016/s0031-3203(02)00027-4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.8060

B. Chalmond, An iterative Gibbsian technique for reconstruction of m-ary images, Pattern Recognition, vol.22, issue.6, p.747761, 1989.
DOI : 10.1016/0031-3203(89)90011-3

D. Clayton and L. Bernadinelli, Bayesian Methods for Mapping Disease Risk. Geographical and Environment Epidemiology: Methods for Small Area Studies, p.205220, 1992.
DOI : 10.1093/acprof:oso/9780192622358.003.0018

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Royal Statistical Society Series B, vol.39, issue.1, p.1, 1977.

C. Ducrot, M. Arnold, A. De-koejer, D. Heim, and D. Calavas, Review on the epidemiology and dynamics of BSE epidemics. Veterinary Research, p.15, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00379857

C. Fernandez and P. Green, Modelling spatially correlated data via mixtures: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.92, issue.4, pp.805-826, 2002.
DOI : 10.1111/1467-9868.00288

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.5651

F. Forbes and N. Peyrard, Hidden Markov Model Selection based on Mean Field like approximations, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.25, issue.8, 2003.
DOI : 10.1109/tpami.2003.1227985

C. Fraley and A. Raftery, Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering, Journal of Classification, vol.24, issue.2, p.155181, 2007.
DOI : 10.1007/s00357-007-0004-5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8693

O. Francois, S. Ancelet, and G. Guillot, Bayesian clustering using hidden Markov random elds in spatial population genetics, Genetics, vol.174, p.885816, 2006.

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, Transaction on Pattern analysis and Machine Int, vol.6, issue.6, p.721741, 1984.

P. J. Green and S. Richardson, Hidden Markov Models and Disease Mapping, Journal of the American Statistical Association, vol.97, issue.460, p.116, 2002.
DOI : 10.1198/016214502388618870

D. Karlis and E. Xekalaki, Choosing initial values for the EM algorithm for nite mixtures, Computational Statistics and Data Analysis, vol.41, p.577590, 2003.

L. Knorr-held and G. Rasser, Bayesian Detection of Clusters and Discontinuities in Disease Maps, Biometrics, vol.92, issue.20, p.1321, 2000.
DOI : 10.1111/j.0006-341X.2000.00013.x

L. Knorr-held, G. Rasser, and N. Becker, Disease mapping of stagespecic cancer incidence data, Biometrics, vol.58, issue.3, p.492501, 2002.

L. Knorr-held and S. Richardson, A hierarchical model for space-time surveillance data on meningococcal disease incidence, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.81, issue.2, p.169183, 2003.
DOI : 10.1111/1467-9868.00353

P. Schlattmann and F. Divino, Disease mapping models: an empirical evaluation, Statistics in Medicine, vol.19, p.22172241, 2000.

A. Lawson and H. Song, Bayesian hierarchical modeling of the dynamics of spatio-temporal inuenza season outbreaks, Spatial and Spatio-temporal Epidemiology, vol.1, p.187195, 2010.

Y. Macnab, On Gaussian Markov random elds and Bayesian disease mapping, Statistical Methods in Medical Research, vol.0, p.120, 2010.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

A. W. Mollie, S. Gilks, D. J. Richardson, and . Spiegelhalter, Bayesian Mapping of Disease. Markov Chain Monte Carlo in Practice, p.359379, 1996.

A. Mollie, Bayesian and Empirical Bayes approaches to disease mapping, 1999.

A. Mollie and S. Richardson, Empirical bayes estimates of cancer mortality rates using spatial models, Statistics in Medicine, vol.1, issue.1, p.95112, 1991.
DOI : 10.1002/sim.4780100114

C. Pascutto, J. Wakeeld, N. Best, S. Richardson, L. Bernardinelli et al., Statistical issues in the analysis of disease mapping data, Statistics in Medicine, vol.14, issue.17-18, p.24932519, 2000.
DOI : 10.1002/1097-0258(20000915/30)19:17/18<2493::AID-SIM584>3.0.CO;2-D

M. Paul, D. Abrial, N. Jarrige, S. Rican, M. Garrido et al., Bovine Spongiform Encephalopathy and Spatial Analysis of the Feed Industry, Emerging Infectious Diseases, vol.13, issue.6, p.867872, 2007.
DOI : 10.3201/eid1306.061169

URL : https://hal.archives-ouvertes.fr/hal-00378799

W. Qian and D. Titterington, Estimation of Parameters in Hidden Markov Models, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.337, issue.1647, p.407428, 1991.
DOI : 10.1098/rsta.1991.0132

S. Richardson, C. ;. Monfort, M. Draper, G. Muirhead, and C. , Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain, Statistics in Medicine, vol.336, issue.21-22, p.24872501, 1995.
DOI : 10.1002/sim.4780142116

C. Robertson, T. Nelson, Y. Macnab, and A. Lawson, Review of methods for space???time disease surveillance, Spatial and Spatio-temporal Epidemiology, vol.1, issue.2-3, p.105116, 2010.
DOI : 10.1016/j.sste.2009.12.001

M. Vignes and F. Forbes, Gene Clustering via Integrated Markov Models Combining Individual and Pairwise Features, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, issue.2, p.260270, 2009.
DOI : 10.1109/TCBB.2007.70248

URL : https://hal.archives-ouvertes.fr/hal-00781174