A. Sotiras, C. Davatzikos, and N. Paragios, Deformable Medical Image Registration: A Survey, IEEE Transactions on Medical Imaging, vol.32, issue.7, pp.1153-1190, 2013.
DOI : 10.1109/TMI.2013.2265603
URL : https://hal.archives-ouvertes.fr/hal-00684715

J. C. Gee and R. K. Bajcsy, Elastic Matching, Brain warping, vol.2, 1998.
DOI : 10.1016/B978-012692535-7/50087-2

C. Broit, Optimal registration of deformed images, 1981.

P. Risholm, F. Janoos, I. Norton, A. J. Golby, W. M. Wells et al., Bayesian characterization of uncertainty in intra-subject non-rigid registration, Medical Image Analysis, vol.17, issue.5, pp.538-555, 2013.
DOI : 10.1016/j.media.2013.03.002

I. J. Simpson, J. A. Schnabel, A. R. Groves, J. L. Andersson, and M. W. Woolrich, Probabilistic inference of regularisation in non-rigid registration, NeuroImage, vol.59, issue.3, pp.2438-2451, 2012.
DOI : 10.1016/j.neuroimage.2011.09.002

I. Simpson, M. Cardoso, M. Modat, D. Cash, M. Woolrich et al., Probabilistic nonlinear registration with spatially adaptive regularisation, Medical image analysis, 2015.

F. J. Richard, A. M. Samson, and C. A. Cuénod, A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences, Statistics and Computing, vol.8, issue.1, 2009.
DOI : 10.1007/s11222-008-9106-7
URL : https://hal.archives-ouvertes.fr/hal-00187723

M. Zhang, N. Singh, and P. T. Fletcher, Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration, Information Processing in Medical Imaging, pp.37-48, 2013.
DOI : 10.1007/978-3-642-38868-2_4

L. , L. Folgoc, H. Delingette, A. Criminisi, and N. Ayache, Sparse bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements, " under revision in Medical Image Analysis Available: https, 2016.

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. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach et al., Nonrigid registration using free-form deformations: application to breast MR images, IEEE Transactions on Medical Imaging, vol.18, issue.8, pp.712-721, 1999.
DOI : 10.1109/42.796284

J. Ashburner, A fast diffeomorphic image registration algorithm, NeuroImage, vol.38, issue.1, pp.95-113, 2007.
DOI : 10.1016/j.neuroimage.2007.07.007

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images Pattern Analysis and Machine Intelligence, IEEE Transactions, issue.6, pp.721-741, 1984.

S. Chib, Marginal Likelihood from the Gibbs Output, Journal of the American Statistical Association, vol.92, issue.432, pp.1313-1321, 1995.
DOI : 10.1080/01621459.1995.10476591

A. Gelman, X. Meng, G. O. Roberts, and R. L. Tweedie, Simulating normalizing constants: From importance sampling to bridge sampling to path sampling Statistical science Exponential convergence of langevin distributions and their discrete approximations, Bernoulli, pp.163-185, 1996.

M. Girolami and B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011.
DOI : 10.1111/j.1467-9868.2010.00765.x

Y. Zhang and C. A. Sutton, Quasi-newton methods for markov chain monte carlo, Advances in Neural Information Processing Systems, pp.2393-2401, 2011.

K. P. Murphy, Machine learning: a probabilistic perspective, 2012.

G. O. Roberts and J. S. Rosenthal, Harris recurrence of metropoliswithin-gibbs and trans-dimensional markov chains, The Annals of Applied Probability, pp.2123-2139, 2006.

T. J. Mitchell and J. J. Beauchamp, Bayesian Variable Selection in Linear Regression, Journal of the American Statistical Association, vol.51, issue.404, pp.1023-1032, 1988.
DOI : 10.1080/01621459.1982.10477809

M. E. Tipping, Sparse bayesian learning and the relevance vector machine, The Journal of Machine Learning Research, vol.1, pp.211-244, 2001.

M. E. Tipping and A. C. , Fast marginal likelihood maximisation for sparse bayesian models, Workshop on artificial intelligence and statistics, 2003.

U. Grenander and M. I. Miller, Representations of knowledge in complex systems, Journal of the Royal Statistical Society. Series B, pp.549-603, 1994.

P. Schniter, L. C. Potter, and J. Ziniel, Fast bayesian matching pursuit, 2008 Information Theory and Applications Workshop, pp.326-333, 2008.
DOI : 10.1109/ITA.2008.4601068

C. E. Rasmussen and J. Quinonero-candela, Healing the relevance vector machine through augmentation, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.689-696, 2005.
DOI : 10.1145/1102351.1102438

M. E. Leventon and W. E. Grimson, Multi-modal volume registration using joint intensity distributions, Medical Image Computing and Computer-Assisted InterventationMICCAI98, pp.1057-1066, 1998.
DOI : 10.1016/S1361-8415(01)80004-9

A. Guimond, A. Roche, N. Ayache, and J. Meunier, Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.58-69, 2001.
DOI : 10.1109/42.906425
URL : https://hal.archives-ouvertes.fr/cea-00333653

F. Janoos, P. Risholm, W. Wells, and I. , Bayesian Characterization of Uncertainty in Multi-modal Image Registration, Biomedical Image Registration, pp.50-59, 2012.
DOI : 10.1007/978-3-642-31340-0_6