R. Francis, R. Bach, and M. I. Jordan, Learning Graphical Models with Mercer Kernels, Advances in Neural Information Processing Systems, pp.1009-1016, 2002.

M. Bertrand-retali, Convergence uniforme d'un estimateur de la densité par la méthode du noyau, Revue Roumaine de Mathématiques Pures et Appliqués, pp.361-385, 1978.

J. Besag, Statistical Analysis of Non-Lattice Data. The Statistician, pp.179-195, 1975.

C. M. Bishop, Pattern Recognition and Machine Learning, 2006.

W. J. Dixon, Simplified Estimation from Censored Normal Samples, The Annals of Mathematical Statistics, vol.31, issue.2, pp.385-391, 1960.
DOI : 10.1214/aoms/1177705900
URL : http://projecteuclid.org/download/pdf_1/euclid.aoms/1177705900

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2001.

P. Olivier and . Faugeras, A Quantile-Copula Approach to Conditional Density Estimation, Journal of Multivariate Analysis, vol.100, pp.2083-2099, 2009.

A. Freno, E. Trentin, and M. Gori, Scalable pseudo-likelihood estimation in hybrid random fields, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.319-327, 2009.
DOI : 10.1145/1557019.1557059

A. Freno, E. Trentin, and M. Gori, Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation, 19th European Conference on Artificial Intelligence, pp.427-432, 2010.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

R. Hofmann and V. Tresp, Discovering Structure in Continous Variables Using Bayesian Networks, Advances in Neural Information Processing Systems, pp.500-506, 1995.

R. Hofmann and V. Tresp, Nonlinear Markov Networks for Continous Variables, Advances in Neural Information Processing Systems, 1997.

W. Kaplan, Advanced Calculus, 1984.

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, 2009.

L. Steffen and . Lauritzen, Graphical Models, 1996.

H. Liu, J. Lafferty, and L. Wasserman, The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs, Journal of Machine Learning Research, vol.10, pp.2295-2328, 2009.

D. Margaritis, Distribution-Free Learning of Bayesian Network Structure in Continuous Domains, AAAI, pp.825-830, 2005.

R. B. Nelsen, An Introduction to Copulas
DOI : 10.1007/978-1-4757-3076-0

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472

P. Ravikumar, G. Raskutti, M. Wainwright, and B. Yu, Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of ? 1 -regularized MLE, Advances in Neural Information Processing Systems, pp.1329-1336, 2008.

M. Rosenblatt, Conditional Probability Density and Regression Estimators, Multivariate Analysis, pp.25-31, 1969.

W. Bernard and . Silverman, Weak and Strong Uniform Consistency of the Kernel Estimate of a Density and Its Derivatives, The Annals of Statistics, vol.6, pp.177-184, 1978.

W. Bernard and . Silverman, Addendum to Weak and Strong Uniform Consistency of the Kernel Estimate of a Density and Its Derivatives, The Annals of Statistics, vol.8, pp.1175-1176, 1980.

W. Bernard and . Silverman, Density Estimation for Statistics and Data Analysis, 1986.

A. Sklar, Fonctions de répartitionrépartitionà n dimensions et leurs marges Publications de l, pp.229-231, 1959.

D. Strauss and M. Ikeda, Pseudolikelihood Estimation for Social Networks, Journal of the American Statistical Association, vol.28, issue.409, pp.204-212, 1990.
DOI : 10.1111/j.2044-8317.1986.tb00844.x