H. Ayasso, Une approche bayésienne de l'inversion. ApplicationàApplicationà l'imagerie de diffraction dans les domaines micro-onde et optique, 2010.

H. Ayasso, B. Duchêne, and A. Mohammad-djafari, Optical diffraction tomography within a variational Bayesian framework, Inverse Problems in Science and Engineering, vol.57, issue.1, pp.59-73, 2012.
DOI : 10.1088/0266-5611/13/6/013
URL : https://hal.archives-ouvertes.fr/hal-00638296

H. Ayasso, B. Duchêne, and A. Mohammad-djafari, MCMC and Variational Approaches for Bayesian Inversion in Diffraction Imaging, Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing, pp.201-224, 2015.
DOI : 10.1002/9781118827253.ch8
URL : https://hal.archives-ouvertes.fr/hal-01262038

V. Beffara and H. Duminil-copin, The self-dual point of the two-dimensional random-cluster model is critical for q ? 1. Probability Theory and Related Fields, pp.3-4, 2012.
URL : https://hal.archives-ouvertes.fr/ensl-00495872

W. C. Chew, Waves and fields in inhomogeneous media, 1995.

D. Colton and R. Kress, Inverse acoustic and electromagnetic scattering theory, 1992.

O. Féron, B. Duchêne, and A. Mohammad-djafari, Microwave imaging of inhomogeneous objects made of a finite number of dielectric and conductive materials from experimental data, Inverse Problems, vol.21, issue.6, pp.95-115, 2005.
DOI : 10.1088/0266-5611/21/6/S08

A. E. Fouda and F. L. Teixeira, Ultra-wideband microwave imaging of breast cancer tumors via Bayesian inverse scattering, Journal of Applied Physics, vol.115, issue.6, p.64701, 2014.
DOI : 10.1063/1.4865327

A. Fraysse and T. Rodet, A Measure-Theoretic Variational Bayesian Algorithm for Large Dimensional Problems, SIAM Journal on Imaging Sciences, vol.7, issue.4, pp.2591-2622, 2014.
DOI : 10.1137/140966575
URL : https://hal.archives-ouvertes.fr/hal-00702259

S. C. Hagness, E. C. Fear, and A. Massa, Guest Editorial: Special Cluster on Microwave Medical Imaging, IEEE Antennas and Wireless Propagation Letters, vol.11, pp.1592-1597, 2012.
DOI : 10.1109/LAWP.2013.2240569

R. F. Harrington, Field computation by moment methods. The Macmillan Company, 1968.

A. M. Hassan and M. El-shenawee, Review of Electromagnetic Techniques for Breast Cancer Detection, IEEE Reviews in Biomedical Engineering, vol.4, pp.103-118, 2011.
DOI : 10.1109/RBME.2011.2169780

G. E. Hinton and D. Van-camp, Keeping the neural networks simple by minimizing the description length of the weights, Proceedings of the sixth annual conference on Computational learning theory , COLT '93, pp.5-13, 1993.
DOI : 10.1145/168304.168306

W. T. Joines, R. L. Jirtle, M. D. Rafal, and D. J. Schaefer, Microwave power absorption differences between normal and malignant tissue, International Journal of Radiation Oncology*Biology*Physics, vol.6, issue.6, pp.681-687, 1980.
DOI : 10.1016/0360-3016(80)90223-0

M. Lazebnik, L. Mccartney, D. Popovic, C. B. Watkins, M. J. Lindstrom et al., A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries, Physics in Medicine and Biology, vol.52, issue.10, pp.52-2637, 2007.
DOI : 10.1088/0031-9155/52/10/001

M. Lazebnik, M. Okoniewski, J. H. Booske, and S. C. Hagness, Highly Accurate Debye Models for Normal and Malignant Breast Tissue Dielectric Properties at Microwave Frequencies, IEEE Microwave and Wireless Components Letters, vol.17, issue.12, pp.822-824, 2007.
DOI : 10.1109/LMWC.2007.910465

M. Lazebnik, D. Popovic, L. Mccartney, C. B. Watkins, M. J. Lindstrom et al., A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries, Physics in Medicine and Biology, vol.52, issue.20, pp.52-6093, 2007.
DOI : 10.1088/0031-9155/52/20/002

D. J. Mackay, Information theory, inference, and learning algorithms, 2003.

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

A. Mohammad-djafari, Gauss-Markov-Potts priors for images in computer tomography resulting to joint optimal reconstruction and segmentation, International Journal of Tomography and Statistics, vol.11, pp.76-92, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00447139

V. Smídlsmídl and A. Quinn, The variational Bayes method in signal processing, 2006.

C. Tai, Dyadic green functions in electromagnetic theory, 1993.

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

P. M. Van-den-berg and R. E. Kleinman, A contrast source inversion method, Inverse Problems, vol.13, issue.6, pp.1607-1620, 1997.
DOI : 10.1088/0266-5611/13/6/013

P. M. Van-den-berg, A. Van-broekhoven, and A. Abubakar, Extended contrast source inversion, Inverse Problems, vol.15, issue.5, pp.1325-1344, 1999.
DOI : 10.1088/0266-5611/15/5/315

E. Zastrow, S. K. Davis, M. Lazebnik, F. Kelcz, B. D. Van-veem et al., Database of 3D grid-based numerical breast phantoms for use in computational electromagnetics simulations, 2008.