D. Barber, Bayesian Reasoning and Machine Learning, 2012.

L. Baringhaus and C. Franz, On a new multivariate two-sample test, Journal of Multivariate Analysis, vol.88, pp.190-206, 2004.

E. Bernton, P. E. Jacob, M. Gerber, and C. P. Robert, Approximate Bayesian computation with the Wasserstein distance, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.81, pp.235-269, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02274870

A. Beygelzimer, S. Kakadet, J. Langford, S. Arya, D. Mount et al., FNN: Fast Nearest Neighbor Search miller and Applications, 2013.

S. Boltz, É. Debreuve, and M. Barlaud, High-dimensional statistical measure for region-of-interest tracking, IEEE Transactions on Image Processing, vol.18, pp.1266-1283, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00417652

R. Crackel and J. Flegal, Bayesian inference for a flexible class of bivariate beta distributions, Journal of Statistical Computation and Simulation, vol.87, pp.295-312, 2017.

A. Dasgupta, Probability for Statistics and Machine Learning: Fundamentals and Advance, 2011.

C. Dellacherie and P. Meyer, Probability and Potential B: Theory of Martingales, 1980.

C. C. Drovandi and A. N. Pettitt, Likelihood-free Bayesian estimation of multivariate quantile distributions, Computational Statistics and Data Analysis, vol.55, pp.2541-2556, 2011.

J. K. Ghosh, M. Delampady, and T. Samanta, An Introduction to Bayesian Analysis: Theory and Methods, 2006.

J. K. Ghosh and R. V. Ramamoorthi, , 2003.

S. Ghosh and A. Van-der-vaart, Fundamentals of Nonparametric Bayesian Inference, 2017.

R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete Mathematics, 1994.

A. Gretton, K. M. Bogwardt, M. Rasch, B. Scholkopf, and A. J. Smola, A kernel method for the two-sample-problem, Advances in Neural Information Processing Systems, 2007.

A. Gretton, K. M. Bogwardt, M. J. Rasch, B. Scholkopf, and A. Smola, A kernel two-sample test, Journal of Machine Learning Research, vol.13, pp.723-773, 2012.

A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur, A fast, consistent kernel two-sample test, Advances in Neural Information Processing Systems, 2009.

N. L. Hjort, C. Holmes, P. Muller, and S. G. Walker, Bayesian Nonparametrics, 2010.

B. Jiang, T. Wu, and W. H. Wong, Approximate Bayesian computation with Kullback-Leibler divergence as data discrepancy, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, 2018.

G. Karabatsos and F. Leisen, An approximate likelihood perspective on ABC methods, Statistical Surveys, vol.12, pp.66-104, 2018.

K. Koch, Introduction to Bayesian Statistics, 2007.

G. Koop, D. J. Poirier, and J. L. Tobias, Bayesian Econometric Methods, 2007.

V. S. Koroljuk and Y. V. Borovskich, Theory of U-Statistics, 1994.

J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander, Fundamentals and recent developments in approximate Bayesian computation, Systems Biology, vol.60, pp.60-82, 2017.

J. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder, Approximate Bayesian computation methods, Statistics and Computing, vol.22, pp.1167-1180, 2012.

J. W. Miller and D. B. Dunson, Robust Bayesian inference via coarsening, Journal of the American Statistical Association, vol.113, pp.340-356, 2018.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

M. Park, W. Jitkrittum, and D. Sejdinovic, K2-ABC: approximate Bayesian computation with kernel embeddings, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016.

S. J. Press, Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, 2003.

J. K. Pritchard, M. T. Seielstad, A. Perez-lezaun, and M. W. Feldman, Population growth of human Y chromosomes: a study of Y chromosome microsatellites, Molecular Biology and Evolution, vol.16, pp.1791-1798, 1999.

G. Puccetti, An algorithm to approximate the optimal expected inner product of two vectors with given marginals, Journal of Mathematical Analysis and Applications, vol.451, issue.1, pp.132-145, 2017.

C. P. Robert, The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, 2007.

D. B. Rubin, Bayesian justifiable and relevant frequency calculations for the applied statistician, Annals of Statistics, vol.12, pp.1151-1172, 1984.

D. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu, Equivalence of distance-based and RKHS-based statistics in hypothesis testing, Annals of Statistics, vol.41, pp.2263-2291, 2013.

P. K. Sen, Almost sure convergence of generalized U-statistics, Annals of Probability, vol.5, pp.287-290, 1977.

R. J. Serfling, Approximation Theorems of Mathematical Statistics, 1980.

S. A. Sisson, Y. Fan, and . Beaumont, Handbook of Approximate Bayesian Computation, 2019.

G. J. Szekely and M. L. Rizzo, Testing for equal distributions in high dimension, InterStat, vol.5, pp.1-16, 2004.

G. J. Szekely and M. L. Rizzo, Energy statistics: a class of statistics based on distances, Journal of Statistical Planning and Inference, vol.143, pp.1249-1272, 2013.

G. J. Szekely and M. L. Rizzo, The energy of data, Annual Review of Statistics and Its Application, vol.4, pp.447-479, 2017.

S. Tavaré, Handbook of Approximate Bayesian Computation, 2019.

S. Tavaré, D. J. Balding, R. C. Griffiths, and P. Donnelly, Inferring coalescence times from DNA sequence data, Genetics, vol.145, pp.505-518, 1997.

J. Voss, An Introduction to Statistical Computing: A Simulation-based Approach, 2014.

A. Zygmund, An individual ergodic theorem for non-comutative transformations, Acta Scientiarum Mathematicarum (Szeged), vol.14, pp.103-110, 1951.