C. E. Antoniak, Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems, The Annals of Statistics, vol.2, pp.1152-1174, 1974.

J. Arbel, P. De-blasi, and I. Prünster, Stochastic approximations to the Pitman-Yor process, Bayesian Analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950654

J. Arbel, S. Favaro, B. Nipoti, and Y. W. Teh, Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics, Statistica Sinica, vol.27, pp.839-858, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01203324

F. Bassetti, R. Casarin, and F. Leisen, Beta-product dependent Pitman-Yor processes for Bayesian inference, Journal of Econometrics, vol.180, issue.1, pp.49-72, 2014.

M. Battiston, S. Favaro, D. M. Roy, and Y. W. Teh, A characterization of product-form exchangeable feature probability functions, The Annals of Applied Probability, vol.28, issue.3, pp.1423-1448, 2018.

A. Canale, A. Lijoi, B. Nipoti, and I. Prünster, On the Pitman-Yor process with spike and slab base measure, Biometrika, vol.104, issue.3, pp.681-697, 2017.

F. Caron, W. Neiswanger, F. Wood, A. Doucet, D. et al., Generalized Pólya Urn for Time-Varying Pitman-Yor Processes, Journal of Machine Learning Research, vol.18, issue.27, pp.1-32, 2017.

A. Clauset, C. R. Shalizi, and M. E. Newman, Power-law distributions in empirical data, SIAM review, vol.51, issue.4, pp.661-703, 2009.

P. De-blasi, S. Favaro, A. Lijoi, R. H. Mena, I. Prünster et al., , 2015.

, Are Gibbs-type priors the most natural generalization of the Dirichlet process? Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.37, issue.2, pp.212-229

B. Derrida, Random-energy model: An exactly solvable model of disordered systems, Physical Review B, vol.24, issue.5, p.2613, 1981.

S. Favaro, A. Lijoi, R. Mena, and I. Prünster, Bayesian non-parametric inference for species variety with a two-parameter Poisson-Dirichlet process prior, J. R. Stat. Soc. Ser. B, vol.71, pp.993-1008, 2009.

S. Favaro and S. G. Walker, Slice sampling ?-stable Poisson-Kingman mixture models, Journal of Computational and Graphical Statistics, vol.22, issue.4, pp.830-847, 2013.

S. Feng and W. Sun, Some diffusion processes associated with two parameter Poisson-Dirichlet distribution and Dirichlet process. Probability theory and related fields, vol.148, pp.501-525, 2010.

T. Ferguson, A Bayesian analysis of some nonparametric problems, The Annals of Statistics, vol.1, issue.2, pp.209-230, 1973.

H. Ishwaran and L. F. James, Gibbs sampling methods for stick-breaking priors, J. Amer. Statist. Assoc, vol.96, pp.161-173, 2001.

A. Jara, E. Lesaffre, M. De-iorio, Q. , and F. , Bayesian semiparametric inference for multivariate doubly-interval-censored data, Ann. Appl. Stat, vol.4, issue.4, pp.2126-2149, 2010.

S. V. Kerov, Coherent random allocations, and the Ewens-Pitman formula, Journal of Mathematical sciences, vol.138, issue.3, pp.5699-5710, 2006.

A. Lo, On a class of Bayesian nonparametric estimates: I. Density estimates, The Annals of Statistics, vol.12, issue.1, pp.351-357, 1984.

J. W. Miller, An elementary derivation of the Chinese restaurant process from Sethuraman's stick-breaking process, 2018.

J. W. Miller and M. T. Harrison, Inconsistency of Pitman-Yor process mixtures for the number of components, The Journal of Machine Learning Research, vol.15, issue.1, pp.3333-3370, 2014.

C. Navarrete, F. A. Quintana, and P. Mueller, Some issues in nonparametric Bayesian modeling using species sampling models, Statistical Modelling, vol.8, issue.1, pp.3-21, 2008.

Y. Ni, P. Müller, Y. Zhu, J. , and Y. , Heterogeneous reciprocal graphical models, Biometrics, vol.74, issue.2, pp.606-615, 2018.

M. Perman, J. Pitman, Y. , and M. , Size-biased sampling of Poisson point processes and excursions. Probability Theory and Related Fields, vol.92, pp.21-39, 1992.

L. Petrov, Two-parameter family of diffusion processes in the Kingman simplex, Functional Analysis and Its Applications, vol.43, pp.279-296, 2009.

J. Pitman, Exchangeable and partially exchangeable random partitions. Probability Theory and Related Fields, vol.102, pp.145-158, 1995.

J. Pitman, Poisson-Kingman partitions, Lecture Notes-Monograph Series, pp.1-34, 2003.

J. Pitman and M. Yor, The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator, The Annals of Probability, vol.25, issue.2, pp.855-900, 1997.

B. Scarpa and D. B. Dunson, Bayesian hierarchical functional data analysis via contaminated informative priors, Biometrics, vol.65, issue.3, pp.772-780, 2009.

C. Scricciolo, Adaptive Bayesian Density Estimation in L p-metrics with Pitman-Yor or Normalized Inverse-Gaussian Process Kernel Mixtures, Bayesian Analysis, vol.9, issue.2, pp.475-520, 2014.
DOI : 10.1214/14-ba863

URL : https://doi.org/10.1214/14-ba863

J. Sethuraman, A constructive definition of Dirichlet priors, Statistica Sinica, vol.4, pp.639-650, 1994.
DOI : 10.21236/ada238689

URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a238689.pdf

E. B. Sudderth and M. I. Jordan, Shared segmentation of natural scenes using dependent Pitman-Yor processes, Advances in Neural Information Processing Systems 21, pp.1585-1592, 2009.

Y. W. Teh, A hierarchical Bayesian language model based on Pitman-Yor processes, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp.985-992, 2006.
DOI : 10.3115/1220175.1220299

URL : http://dl.acm.org/ft_gateway.cfm?id=1220299&type=pdf

A. Vershik, M. Yor, and N. Tsilevich, On the Markov-Krein identity and quasiinvariance of the gamma process, Journal of Mathematical Sciences, vol.121, issue.3, pp.2303-2310, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00102239

F. Wood, J. Gasthaus, C. Archambeau, L. James, and Y. W. Teh, The sequence memoizer, Communications of the ACM, vol.54, issue.2, pp.91-98, 2011.
DOI : 10.1145/1897816.1897842