J. Arbel, P. D. Blasi, and I. Prünster, Stochastic approximations to the Pitman-Yor process, Bayesian Analysis, vol.14, issue.3, pp.753-771, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01950654

, Section 2

J. Arbel and I. Prünster, A moment-matching Ferguson & Klass algorithm, Statistics and Computing, vol.27, issue.1, pp.3-17, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01396587

J. Arbel and I. Prünster, Truncation error of a superposed gamma process in a decreasing order representation. NeurIPS Advances in Approximate Bayesian Inference workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405580

J. Arbel and I. Prünster-;-raffaele-argiento, Bayesian Statistics in Action, chapter On the truncation error of a superposed gamma process, Springer Proceedings in Mathematics & Statistics, vol.194, pp.11-19, 2017.

J. Arbel and S. Favaro, Approximating predictive probabilities of Gibbstype priors, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01667746

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

, Section 2.4

H. D. Nguyen, J. Arbel, H. Lü, and F. Forbes, Approximate Bayesian computation via the energy statistic, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02399934

J. Arbel and I. Prünster, A moment-matching Ferguson & Klass algorithm, Statistics and Computing, vol.27, issue.1, pp.3-17, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01396587

J. Arbel and I. Prünster, Truncation error of a superposed gamma process in a decreasing order representation. NeurIPS Advances in Approximate Bayesian Inference workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405580

J. Arbel and I. Prünster-;-raffaele-argiento, Bayesian Statistics in Action, chapter On the truncation error of a superposed gamma process, Springer Proceedings in Mathematics & Statistics, vol.194, pp.11-19, 2017.

J. Arbel and S. Favaro, Approximating predictive probabilities of Gibbstype priors, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01667746

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

O. Marchal and J. , On the sub-Gaussianity of the Beta and Dirichlet distributions, Electronic Communications in Probability, vol.22, pp.1-14, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01521300

J. Arbel, O. Marchal, and H. D. Nguyen, On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables, ESAIM: Probability & Statistics, forthcoming, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01998252

M. Vladimirova and J. , Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions, 2019.

M. Vladimirova, J. Verbeek, P. Mesejo, and J. Arbel, Understanding Priors in Bayesian Neural Networks at the Unit Level, vol.ICML, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02177151

M. Vladimirova, J. Arbel, and P. Mesejo, Bayesian neural networks become heavier-tailed with depth, NeurIPS Bayesian Deep Learning Workshop, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950658

M. Vladimirova, J. Arbel, and P. Mesejo, Bayesian neural network priors at the level of units, 1st Symposium on Advances in Approximate Bayesian Inference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950659

J. Arbel, M. Crispino, and S. Girard, Dependence properties and Bayesian inference for asymmetric multivariate copulas, Journal of Multivariate Analysis, vol.174, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02413948

T. Rahier, , p.12

?. Rajkowski, . Robert, . Christian, and . Rousseau,

F. Ruggeri and . Ruggiero,

R. Ryder and . Publication,

J. Arbel and S. Favaro, Approximating predictive probabilities of Gibbs-type priors, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01667746

J. Arbel, O. Marchal, and H. Nguyen, On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables, ESAIM: Probability & Statistics, forthcoming, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01998252

J. Arbel, M. Crispino, and S. Girard, Dependence properties and Bayesian inference for asymmetric multivariate copulas, Journal of Multivariate Analysis, vol.174, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02413948

M. Vladimirova, J. Verbeek, P. Mesejo, and J. Arbel, Understanding Priors in Bayesian Neural Networks at the Unit Level. ICML, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02177151

C. Lawless and J. Arbel, A simple proof of Pitman-Yor's Chinese restaurant process from its stick-breaking representation, Dependence Modeling, vol.7, 2019.

J. Arbel, I. Pierpaolo-de-blasi, and . Prünster, Stochastic approximations to the Pitman-Yor process, Bayesian Analysis, vol.14, issue.3, pp.753-771, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01950654

O. Marchal and J. Arbel, On the sub-Gaussianity of the Beta and Dirichlet distributions, Electronic Communications in Probability, vol.22, pp.1-14, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01521300

J. Arbel and I. Prünster, A moment-matching Ferguson & Klass algorithm, Statistics and Computing, vol.27, issue.1, pp.3-17, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01396587

J. Arbel, S. Favaro, B. Nipoti, and Y. 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

J. Arbel, K. Mengersen, and J. Rousseau, Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity, Annals of Applied Statistics, vol.10, issue.3, pp.1496-1516, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01203345

J. Arbel and V. Costemalle, Estimation of immigration flows : reconciling two sources by a Bayesian approach, Économie et Statistique, vol.485, pp.121-149, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01396606

J. Arbel, A. Lijoi, and B. Nipoti, Full Bayesian inference with hazard mixture models, Computational Statistics & Data Analysis, vol.93, pp.359-372, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01203296

J. Arbel, K. Mengersen, B. Raymond, T. Winsley, and C. King, Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel contaminated soil, Ecology and Evolution, vol.5, issue.13, pp.2633-2645, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01203289

J. Arbel, G. Gayraud, and J. Rousseau, Bayesian optimal adaptive estimation using a sieve prior, Scandinavian Journal of Statistics, vol.40, issue.3, pp.549-570, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01203280

. Book,

D. Fraix, -. Burnet, S. Girard, J. Arbel, and J. , Book Chapters, Statistics for Astrophysics: Bayesian Methodology, 2018.

J. Arbel, Clustering Milky Way's Globulars: a Bayesian Nonparametric Approach, chapter, Statistics for Astrophysics: Bayesian Methodology. EDP Sciences, 2018.

K. Mengersen, C. Alston, J. Arbel, and E. Duncan, Applications in Industry, chapter in Handbook of mixture analysis, 2018.

G. King, J. Arbel, and I. Prünster, Bayesian Statistics in Action, chapter A Bayesian nonparametric approach to ecological risk assessment, Springer Proceedings in Mathematics & Statistics, vol.194, pp.151-159, 2017.

J. Arbel and I. Prünster, Bayesian Statistics in Action, chapter On the truncation error of a superposed gamma process, Springer Proceedings in Mathematics & Statistics, vol.194, pp.11-19, 2017.

J. Arbel, A. Lijoi, and B. Nipoti, Bayesian Statistics from Methods to Models and Applications, chapter Bayesian Survival Model based on Moment Characterization, Springer Proceedings in Mathematics & Statistics, vol.126, pp.3-14, 2015.

J. Arbel, R. Corradin, and M. ?ewandowski, Bayesian Cluster Analysis: Point Estimation and Credible Balls, Bayesian Analysis, vol.13, pp.559-626, 2018.

J. Arbel, Sparse graphs using exchangeable random measures" by Caron and Fox, Journal of the Royal Statistical Society. Series B, vol.79, 2017.

J. Arbel and C. P. Robert, Statistical modelling of citation exchange between statistics journals" by Varin, Cattelan and Firth, Journal of the Royal Statistical Society. Series A, vol.179, pp.41-42, 2016.

J. Arbel and I. Prünster, Discussion of "Sequential Quasi-Monte Carlo" by Gerber and Chopin, Journal of the Royal Statistical Society. Series B, vol.77, pp.559-560, 2015.

J. Arbel and B. Nipoti, Bayesian Nonparametric Inference -Why and How" by Müller and Mitra, Bayesian Analysis, vol.8, issue.02, pp.326-328, 2013.

P. Christian, J. Robert, and . Arbel, Sparse Bayesian regularization and prediction, Bayesian Statistics, vol.9, 2009.

F. Veronica-munoz-ramirez, J. Forbes, A. Arbel, M. Arnaud, and . Dojat, Quantitative MRI Characterization of Brain Abnormalities in 'de novo' Parkinsonian Patients, IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

F. Boux, F. Forbes, J. Arbel, and E. Barbier, Estimation de paramètres IRM en grande dimension via une reégression inverse, Congrès de la Société Française de Résonance Magnétique en Biologie et Médecine (SFRMBM), 2019.

F. Boux, F. Forbes, J. Arbel, and E. Barbier, Apprentissage de dictionnaires par régression: application à l'IRM vasculaire, Congrès National de l'Imagerie du Vivant (CNIV), 2019.

F. Boux, F. Forbes, J. Arbel, and E. Barbier, Dictionary-free MR fingerprinting parameter estimation via inverse regression, International Society for Magnetic Resonance in Medicine (ISMRM), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01941630

M. Vladimirova, J. Arbel, and P. Mesejo, Bayesian neural networks become heavier-tailed with depth, NeurIPS Bayesian Deep Learning Workshop, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950658

M. Vladimirova, J. Arbel, and P. Mesejo, Bayesian neural network priors at the level of units, 1st Symposium on Advances in Approximate Bayesian Inference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950659

H. Lü, J. Arbel, and F. Forbes, Bayesian Nonparametric Priors for Hidden Markov Random Fields, 2018.

J. Arbel and J. Salomond, Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models, NeurIPS Practical Bayesian Nonparametrics workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405568

J. Arbel and I. Prünster, Truncation error of a superposed gamma process in a decreasing order representation, NeurIPS Advances in Approximate Bayesian Inference workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405580

G. King, J. Arbel, and I. Prünster, Bayesian Nonparametric Density Estimation in Ecotoxicology, 2016.

J. Arbel, S. Favaro, B. Nipoti, and Y. Teh, Discovery Probabilities when Uncertainty Matters. 48 e Journées de la Statistique de la SFdS, 2016.

J. Arbel, S. Favaro, B. Nipoti, and Y. Teh, On Bayesian nonparametric inference for discovery probabilities, Proceedings of the 48 th Meeting of the Italian Statistical Society, 2016.

J. Arbel, K. Mengersen, and J. Rousseau, On diversity under a Bayesian nonparametric dependent model, Proceedings of the 47 th Meeting of the Italian Statistical Society, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01203340

J. Arbel, O. Marchal, and B. Nipoti, On the Hurwitz zeta function with an application to the exponential-beta distribution, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02400451

H. Lü, J. Arbel, and F. Forbes, Bayesian Nonparametric Priors for Hidden Markov Random Fields. Under major revision, Statistics and Computing, 2019.

J. Arbel, G. King, A. Lijoi, L. E. Nieto-barajas, and I. Prünster, BNPdensity: Bayesian nonparametric mixture modeling in R, 2019.

J. Hien-d-nguyen, H. Arbel, F. Lü, and . Forbes, Approximate Bayesian computation via the energy statistic, 2019.

M. Vladimirova and J. Arbel, Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions, 2019.

J. Arbel, R. Corradin, and B. Nipoti, Dirichlet process mixtures under affine transformations of the data, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950652

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J. Arbel, R. Corradin, and B. Nipoti, Dirichlet process mixtures under affine transformations of the data, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950652

J. Arbel and V. Costemalle, Estimation of immigration flows : reconciling two sources by a Bayesian approach, Économie et Statistique, vol.485, pp.121-149, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01396606

J. Arbel, M. Crispino, and S. Girard, Dependence properties and Bayesian inference for asymmetric multivariate copulas, Journal of Multivariate Analysis, vol.174, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02413948

J. Arbel, P. D. Blasi, and I. Prünster, Stochastic approximations to the Pitman-Yor process, Bayesian Analysis, vol.14, issue.3, pp.753-771, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01950654

J. Arbel and S. Favaro, Approximating predictive probabilities of Gibbs-type priors, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01667746

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

J. Arbel, G. Gayraud, and J. Rousseau, Bayesian optimal adaptive estimation using a sieve prior, Scandinavian Journal of Statistics, vol.40, issue.3, pp.549-570, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01203280

J. Arbel, G. King, A. Lijoi, L. E. Nieto-barajas, and I. Prünster, BN-Pdensity: Bayesian nonparametric mixture modeling in R, 2019.

J. Arbel, A. Lijoi, and B. Nipoti, Bayesian Statistics from Methods to Models and Applications, chapter Bayesian Survival Model based on Moment Characterization, Springer Proceedings in Mathematics & Statistics, vol.126, pp.3-14, 2015.

J. Arbel, A. Lijoi, and B. Nipoti, Full Bayesian inference with hazard mixture models, Computational Statistics & Data Analysis, vol.93, pp.359-372, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01203296

J. Arbel, O. Marchal, and H. D. Nguyen, On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables, ESAIM: Probability & Statistics, forthcoming, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01998252

J. Arbel, O. Marchal, and B. Nipoti, On the Hurwitz zeta function with an application to the exponential-beta distribution, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02400451

J. Arbel, K. Mengersen, B. Raymond, T. Winsley, and C. King, Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel contaminated soil, Ecology and Evolution, vol.5, issue.13, pp.2633-2645, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01203289

J. Arbel, K. Mengersen, and J. Rousseau, Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity, Annals of Applied Statistics, vol.10, issue.3, pp.1496-1516, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01203345

J. Arbel and B. Nipoti, Bayesian Nonparametric Inference -Why and How" by Müller and Mitra, Bayesian Analysis, vol.8, issue.02, pp.326-328, 2013.

J. Arbel and I. Prünster, Discussion of "Sequential Quasi-Monte Carlo" by Gerber and Chopin, Journal of the Royal Statistical Society. Series B, vol.77, pp.559-560, 2015.

J. Arbel and I. Prünster, Truncation error of a superposed gamma process in a decreasing order representation. NeurIPS Advances in Approximate Bayesian Inference workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01405580

J. Arbel and I. Prünster, A moment-matching Ferguson & Klass algorithm, Statistics and Computing, vol.27, issue.1, pp.3-17, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01396587

J. Arbel and I. Prünster, Bayesian Statistics in Action, chapter On the truncation error of a superposed gamma process, Springer Proceedings in Mathematics & Statistics, vol.194, pp.11-19, 2017.

J. Arbel and C. P. Robert, Discussion of "Statistical modelling of citation exchange between statistics journals" by Varin, Cattelan and Firth, Journal of the Royal Statistical Society. Series A, vol.179, pp.41-42, 2016.

J. Arbel and J. Salomond, Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models, NeurIPS Practical Bayesian Nonparametrics workshop, 2016.
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