C. Botts, An accept-reject algorithm for the positive multivariate normal distribution, Computational Statistics, vol.5, issue.4, pp.1749-1773, 2013.
DOI : 10.1007/s00180-012-0377-2

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

J. Breslaw, Random sampling from a truncated multivariate normal distribution, Applied Mathematics Letters, vol.7, issue.1, pp.1-6, 1994.
DOI : 10.1016/0893-9659(94)90042-6

G. Casella and E. I. George, Explaining the Gibbs sampler, The American Statistician, vol.46, issue.3, pp.167-174, 1992.

N. Chopin, Fast simulation of truncated Gaussian distributions, Statistics and Computing, vol.82, issue.398, pp.275-288, 2011.
DOI : 10.1007/s11222-009-9168-1

D. Veiga, S. Marrel, and A. , Gaussian process modeling with inequality constraints, Annales de la faculté des sciences de Toulouse, pp.529-555, 2012.
DOI : 10.5802/afst.1344

L. Devroye, Non-Uniform Random Variate Generation, 1986.
DOI : 10.1007/978-1-4613-8643-8

N. Ellis and R. Maitra, Multivariate Gaussian Simulation Outside Arbitrary Ellipsoids, Journal of Computational and Graphical Statistics, vol.16, issue.3, pp.692-708, 2007.
DOI : 10.1198/106186007X238431

X. Emery, D. Arroyo, and M. Peláez, Simulating Large Gaussian Random Vectors Subject to Inequality Constraints by Gibbs Sampling, Mathematical Geosciences, vol.22, issue.4, pp.1-19, 2013.
DOI : 10.1007/s11004-013-9495-9

X. Freulon and C. Fouquet, Conditioning a Gaussian model with inequalities, Geostatistics Tróia '92, Quantitative Geology and Geostatistics, pp.201-212, 1993.
DOI : 10.1007/978-94-011-1739-5_17

A. E. Gelfand, A. F. Smith, and T. M. Lee, Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling, Journal of the American Statistical Association, vol.47, issue.418, pp.523-532, 1992.
DOI : 10.1093/biomet/60.2.319

J. Geweke, Exact inference in the inequality constrained normal linear regression model, Journal of Applied Econometrics, vol.59, issue.2, pp.127-141, 1986.
DOI : 10.1002/jae.3950010203

J. Geweke, Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities, Computing Science and Statistics:Proceedings of the 23rd Symposium on the Interface, pp.571-578, 1991.

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. E. Griffiths, A Gibbs sampler for the parameters of a truncated multivariate normal distribution . Department of Economics -Working Papers Series 856, 2002.

W. Hörmann, J. Leydold, and G. Derflinger, Automatic Nonuniform Random Variate Generation, Statistics and Computing, 2004.

J. H. Kotecha and P. Djuric, Gibbs sampling approach for generation of truncated multivariate Gaussian random variables, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), pp.1757-1760, 1999.
DOI : 10.1109/ICASSP.1999.756335

P. W. Laud, P. Damien, and T. S. Shively, Sampling Some Truncated Distributions Via Rejection Algorithms, Communications in Statistics - Simulation and Computation, vol.40, issue.6, pp.1111-1121, 2010.
DOI : 10.2307/1266749

Y. Li and S. K. Ghosh, Efficient sampling method for truncated multivariate normal and Student t-distribution subject to linear inequality constraints URL http

L. Martino and J. Miguez, An adaptive accept/reject sampling algorithm for posterior probability distributions, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp.45-48, 2009.
DOI : 10.1109/SSP.2009.5278644

L. Martino and J. Miguez, A novel rejection sampling scheme for posterior probability distributions, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2921-2924, 2009.
DOI : 10.1109/ICASSP.2009.4960235

A. Philippe and C. P. Robert, Perfect simulation of positive Gaussian distributions, Statistics and Computing, vol.13, issue.2, pp.179-186, 2003.
DOI : 10.1023/A:1023264710933

C. P. Robert, Simulation of truncated normal variables, Statistics and Computing, vol.82, issue.2, 1995.
DOI : 10.1007/BF00143942
URL : https://hal.archives-ouvertes.fr/hal-00431310

C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2004.

G. Rodriguez-yam, R. A. Davis, and L. L. Scharf, Efficient Gibbs Sampling of Truncated Multivariate Normal with Application to Constrained Linear Regression, 2004.

V. Neumann and J. , Various Techniques Used in Connection with Random Digits, J. Res. Nat. Bur. Stand, vol.12, pp.36-38, 1951.

Y. Jun-wu and G. L. , Efficient Algorithms for Generating Truncated Multivariate Normal Distributions, Acta Mathematicae Applicatae Sinica, English Series, vol.27, issue.4, p.601, 2011.