R. Gribonval and P. Machart, Reconciling " priors " & " priors " without prejudice?, Adv. Neural Information Processing Systems (NIPS), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00918008

R. Gribonval and P. Machart, Reconciling " priors " & " priors " without prejudice?, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00918008

E. Arthur, R. W. Hoerl, and . Kennard, Ridge regression: applications to nonorthogonal problems, Technometrics, vol.12, issue.1, pp.69-82, 1970.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, vol.58, issue.1, pp.267-288, 1996.

R. Gribonval, Should Penalized Least Squares Regression be Interpreted as Maximum A Posteriori Estimation?, IEEE Transactions on Signal Processing, vol.59, issue.5, pp.2405-2410, 2011.
DOI : 10.1109/TSP.2011.2107908

URL : https://hal.archives-ouvertes.fr/inria-00486840

C. M. Stein, Estimation of the Mean of a Multivariate Normal Distribution, The Annals of Statistics, vol.9, issue.6, pp.1135-1151, 1981.
DOI : 10.1214/aos/1176345632

Y. Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems . Core discussion papers, Center for Operations Research and Econometrics (CORE), 2010.

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

M. Raphan and E. P. Simoncelli, Learning to be bayesian without supervision, Adv. Neural Information Processing Systems (NIPS), 2007.