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
Ramsés H MenaIIMAS-UNAMVerified email at sigma.iimas.unam.mx
Bernardo NipotiUniversity of Milano Bicocca, ItalyVerified email at unimib.it
Federico CamerlenghiAssistant Professor of Statistics, University of Milano - BicoccaVerified email at unimib.it
Emanuele Doleraricercatore in probabilità e statistica, Università di PaviaVerified email at unipv.it
Boyu RenMcLean HospitalVerified email at partners.org
Maria De IorioProfessor of Biostatistics, National University of Singapore & University College LondonVerified email at nus.edu.sg
Julyan ArbelInriaVerified email at inria.fr
Maria LomeliMetaVerified email at fb.com
Fadhel AyedDepartment of Statistics, University of OxfordVerified email at some.ox.ac.uk
Susan P HolmesProfessor of Statistics, Stanford UniversityVerified email at stat.stanford.edu
Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyVerified email at mit.edu
Tommi VatanenSenior Research fellow, Liggins Institute, University of AucklandVerified email at auckland.ac.nz
Curtis HuttenhowerDepartment of Biostatistics, Harvard School of Public HealthVerified email at hsph.harvard.edu
Daniele BracalePhD StatisticsVerified email at umich.edu
Lloyd T. ElliottSimon Fraser UniversityVerified email at sfu.ca
Zacharie NauletUniversité Paris-SaclayVerified email at universite-paris-saclay.fr
Lancelot F. JamesProfessor, Hong Kong University of Science and TechnologyVerified email at ust.hk
Mingyuan ZhouAssociate Professor, University of Texas at AustinVerified email at mccombs.utexas.edu

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	128	2013
MCMC for normalized random measure mixture models S Favaro, YW Teh Statistical Science 28 (3), 335-359, 2013	115	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	98	2009
A new estimator of the discovery probability S Favaro, A Lijoi, I Prünster Biometrics 68 (4), 1188-1196, 2012	45	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	42	2013
On the stick-breaking representation of normalized inverse Gaussian priors S Favaro, A Lijoi, I Pruenster Biometrika 99 (3), 663-674, 2012	38	2012
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	37	2017
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	33	2011
Slice sampling σ-stable Poisson-Kingman mixture models S Favaro, SG Walker Journal of Computational and Graphical Statistics 22 (4), 830-847, 2013	26	2013
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
Rediscovery of Good–Turing estimators via Bayesian nonparametrics S Favaro, B Nipoti, YW Teh Biometrics 72 (1), 136-145, 2016	18	2016
Bayesian nonparametric analysis of reversible Markov chains S Bacallado, S Favaro, L Trippa The Annals of Statistics, 870-896, 2013	18	2013
On the distribution of sums of independent exponential random variables via Wilks’ integral representation S Favaro, SG Walker Acta applicandae mathematicae 109 (3), 1035-1042, 2010	18	2010
Infinitely deep neural networks as diffusion processes S Peluchetti, S Favaro International Conference on Artificial Intelligence and Statistics, 1126-1136, 2020	16	2020
Sufficientness postulates for Gibbs-type priors and hierarchical generalizations S Bacallado, M Battiston, S Favaro, L Trippa Statistical Science, 487-500, 2017	16	2017
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	16	2017
Stable behaviour of infinitely wide deep neural networks S Peluchetti, S Favaro, S Fortini International Conference on Artificial Intelligence and Statistics, 1137-1146, 2020	15	2020
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	15*	2020
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	15	2017
Modeling population structure under hierarchical Dirichlet processes LT Elliott, M De Iorio, S Favaro, K Adhikari, YW Teh Bayesian Analysis 14 (2), 313-339, 2019	13*	2019

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