Stefano Favaro

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Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
lorenzo trippaHarvard and Dana FarberVerified email at jimmy.harvard.edu
Stefano PeluchettiResearch Scientist, Sakana AIVerified email at sakana.ai
Sergio BacalladoAssociate Professor of Statistics, University of CambridgeVerified email at cam.ac.uk
Bernardo NipotiUniversity of Milano Bicocca, ItalyVerified email at unimib.it
Ramsés H MenaIIMAS-UNAMVerified email at sigma.iimas.unam.mx
Emanuele Doleraricercatore in probabilità e statistica, Università di PaviaVerified email at unipv.it
Lorenzo MasoeroApplied Scientist, AmazonVerified email at mit.edu
Maria De IorioProfessor of Biostatistics, National University of SingaporeVerified email at nus.edu.sg
Matteo SesiaUniversity of Southern CaliforniaVerified email at usc.edu
Boyu RenMcLean HospitalVerified email at partners.org
Maria LomeliMeta, FAIRVerified email at meta.com
Zacharie NauletUniversité Paris-SaclayVerified email at universite-paris-saclay.fr
Julyan ArbelInria - Univ. Grenoble AlpesVerified email at inria.fr
Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyVerified email at mit.edu
Fadhel AyedDepartment of Statistics, University of OxfordVerified email at some.ox.ac.uk
Daniele BracalePhD StatisticsVerified email at umich.edu
Susan P HolmesProfessor of Statistics, Stanford UniversityVerified email at stat.stanford.edu
Mario BerahaDepartment of Mathematics, Politecnico di MilanoVerified email at polimi.it
Alessandra GuglielmiProfessor of Statistics, Department of Mathematics, Politecnico di MilanoVerified email at polimi.it

Stefano Favaro

University of Torino and Collegio Carlo Alberto

Verified email at unito.it - Homepage

Bayesian nonparametric statistics


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Are Gibbs-type priors the most natural generalization of the Dirichlet process? P De Blasi, S Favaro, A Lijoi, RH Mena, I Prünster, M Ruggiero IEEE transactions on pattern analysis and machine intelligence 37 (2), 212-229, 2013	207	2013
MCMC for normalized random measure mixture models S Favaro, YW Teh Statistical Science 28 (3), 335-359, 2013	140	2013
Bayesian non-parametric inference for species variety with a two-parameter Poisson–Dirichlet process prior S Favaro, A Lijoi, RH Mena, I Prünster Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2009	122	2009
A new estimator of the discovery probability S Favaro, A Lijoi, I Prünster Biometrics 68 (4), 1188-1196, 2012	58	2012
On the stick-breaking representation of normalized inverse Gaussian priors S Favaro, A Lijoi, I Prünster Biometrika 99 (3), 663-674, 2012	47	2012
Conditional formulae for Gibbs-type exchangeable random partitions S Favaro, A Lijoi, I Pruenster The Annals of Applied Probability 23 (5), 1721-1754, 2013	46	2013
Bayesian nonparametric ordination for the analysis of microbial communities B Ren, S Bacallado, S Favaro, S Holmes, L Trippa Journal of the American Statistical Association 112 (520), 1430-1442, 2017	43	2017
Infinitely deep neural networks as diffusion processes S Peluchetti, S Favaro International Conference on Artificial Intelligence and Statistics, 1126-1136, 2020	40	2020
On a class of distributions on the simplex S Favaro, G Hadjicharalambous, I Prünster Journal of Statistical Planning and Inference 141 (9), 2987-3004, 2011	39	2011
Slice sampling σ-stable Poisson-Kingman mixture models S Favaro, SG Walker Journal of Computational and Graphical Statistics 22 (4), 830-847, 2013	34	2013
Stable behaviour of infinitely wide deep neural networks S Peluchetti, S Favaro, S Fortini International Conference on Artificial Intelligence and Statistics, 1137-1146, 2020	30	2020
On the stick-breaking representation for homogeneous NRMIs S Favaro, A Lijoi, C Nava, B Nipoti, I Pruenster, YW Teh Bayesian Analysis 11 (3), 697-724, 2016	30	2016
Sufficientness postulates for Gibbs-type priors and hierarchical generalizations S Bacallado, M Battiston, S Favaro, L Trippa Statistical Science, 487-500, 2017	27	2017
Rediscovery of Good–Turing estimators via Bayesian nonparametrics S Favaro, B Nipoti, YW Teh Biometrics 72 (1), 136-145, 2016	24	2016
Alpha-diversity processes and normalized inverse-Gaussian diffusions M Ruggiero, SG Walker, S Favaro The Annals of Applied Probability 23 (1), 386-425, 2013	24	2013
More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics L Masoero, F Camerlenghi, S Favaro, T Broderick Biometrika 109 (1), 17-32, 2022	23	2022
A marginal sampler for σ-stable Poisson–Kingman mixture models M Lomelí, S Favaro, YW Teh Journal of Computational and Graphical Statistics 26 (1), 44-53, 2017	23	2017
Bayesian nonparametric analysis of reversible Markov chains S Bacallado, S Favaro, L Trippa The Annals of Statistics, 870-896, 2013	23	2013
Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis B Ren, S Bacallado, S Favaro, T Vatanen, C Huttenhower, L Trippa The Annals of Applied Statistics 14 (1), 494-517, 2020	22*	2020
Bayesian nonparametric inference for discovery probabilities: Credible intervals and large sample asymptiotics J Arbel, S Favaro, B Nipoti, YW Teh Statistica Sinica, 839-858, 2017	21	2017

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