C. Andrieu, D. Freitas, . Nando, . Doucet, J. Arnaud et al., An introduction to MCMC for machine learning, Machine Learning, pp.5-43, 2003.

S. Bubeck and R. Eldan, The entropic barrier: A simple and optimal universal self-concordant barrier, Conference on Learning Theory, 2015.

M. Dymetman, . Bouchard, . Guillaume, and S. Carter, The OS algorithm: A joint approach to exact optimization and sampling, 2012.

J. Fill and . Allen, An interruptible algorithm for perfect sampling via Markov chains, Annals of Applied Probability, vol.8, issue.1, pp.131-162, 1998.

W. Gilks, Derivative-free adaptive rejection sampling for Gibbs sampling, Bayesian Statistics, vol.4, 1992.

W. R. Gilks and P. Wild, Adaptive Rejection Sampling for Gibbs Sampling, Applied Statistics, vol.41, issue.2, pp.337-348, 1992.
DOI : 10.2307/2347565

W. R. Gilks, N. G. Best, and K. K. Tan, Adaptive Rejection Metropolis Sampling within Gibbs Sampling, Applied Statistics, vol.44, issue.4, pp.455-472, 1995.
DOI : 10.2307/2986138

E. Giné and R. Nickl, Adaptive estimation of a distribution function and its density in sup-norm loss by wavelet and spline projections, Bernoulli, vol.16, issue.4, pp.1137-1163, 2010.
DOI : 10.3150/09-BEJ239

E. Giné and R. Nickl, Confidence bands in density estimation. The Annals of Statistics, pp.1122-1170, 2010.

D. Görür, Y. Teh, and . Whye, Concave-Convex adaptive rejection sampling, Journal of Computational and Graphical Statistics, 2011.

R. Hildebrand, Canonical Barriers on Convex Cones, Mathematics of Operations Research, vol.39, issue.3, pp.841-850, 2014.
DOI : 10.1287/moor.2013.0640

URL : https://hal.archives-ouvertes.fr/hal-01417457

A. Korostelev and M. Nussbaum, The Asymptotic Minimax Constant for Sup-Norm Loss in Nonparametric Density Estimation, Bernoulli, vol.5, issue.6, pp.1099-1118, 1999.
DOI : 10.2307/3318561

L. Martino and J. Míguez, A generalization of the adaptive rejection sampling algorithm, Statistics and Computing, vol.51, issue.5, pp.633-647, 2011.
DOI : 10.1007/s11222-010-9197-9

N. Metropolis and S. Ulam, The Monte Carlo Method, Journal of the American Statistical Association, vol.44, issue.247, pp.335-341, 1949.
DOI : 10.1080/01621459.1949.10483310

T. Minka, Expectation oropagation for approximate Bayesian inference, In Uncertainty in Artificial Intelligence, 2001.

Y. Nesterov, Introductory lectures on convex optimization: A basic course, 2004.
DOI : 10.1007/978-1-4419-8853-9

J. Propp and D. Wilson, Coupling from the oast: A user's guide, Microsurveys in Discrete Probability, 1998.

A. B. Tsybakov, Pointwise and sup-norm sharp adaptive estimation of functions on the Sobolev classes, The Annals of Statistics, vol.26, issue.6, pp.2420-2469, 1998.
DOI : 10.1214/aos/1024691478

P. Zhang, Nonparametric Importance Sampling, Journal of the American Statistical Association, vol.55, issue.435, pp.1245-1253, 1996.
DOI : 10.1214/aos/1176343541