Nonparametric Bayes and empirical Bayes for species sampling problems: classical questions, new directions and related issues

Project Information

NBEB-SSP

Grant agreement ID: 817257

DOI 10.3030/817257

Project closed

EC signature date 14 February 2019

Start date 1 March 2019

End date 29 February 2024

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost € 982 930,00

EU contribution € 982 930,00

Coordinated by

UNIVERSITA DEGLI STUDI DI TORINO
Italy

Publications

Peer reviewed articles (23)

On Johnson’s “Sufficientness” Postulates for Feature-Sampling Models

Author(s): Federico Camerlenghi, Stefano Favaro
Published in: Mathematics, 2021, ISSN 2227-7390
Publisher: MDPI
DOI: 10.3390/math9222891

Upscaling human activity data: A statistical ecology approach

Author(s): Anna Tovo, Samuele Stivanello, Amos Maritan, Samir Suweis, Stefano Favaro, Marco Formentin
Published in: PLOS ONE, Issue 16/7, 2021, Page(s) e0253461, ISSN 1932-6203
Publisher: Public Library of Science
DOI: 10.1371/journal.pone.0253461

Bayesian nonparametric disclosure risk assessment

Author(s): Stefano Favaro, Francesca Panero, Tommaso Rigon
Published in: Electronic Journal of Statistics, 2021, ISSN 1935-7524
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/21-ejs1933

A Compound Poisson Perspective of Ewens–Pitman Sampling Model

Author(s): Emanuele Dolera, Stefano Favaro
Published in: Mathematics, 2021, ISSN 2227-7390
Publisher: MDPI
DOI: 10.3390/math9212820

On consistent and rate optimal estimation of the missing mass

Author(s): Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Published in: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, Issue 57/3, 2021, Page(s) 1476-1494, ISSN 0246-0203
Publisher: Elsevier BV
DOI: 10.1214/20-aihp1126

A Good-Turing estimator for feature allocation models

Author(s): Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Published in: Electronic Journal of Statistics, Issue 13/2, 2019, Page(s) 3775-3804, ISSN 1935-7524
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/19-ejs1614

Nonparametric Bayesian multiarmed bandits for single-cell experiment design

Author(s): Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro
Published in: The Annals of Applied Statistics, Issue 14/4, 2020, Page(s) 2003-2019, ISSN 1932-6157
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/20-aoas1370

A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data

Author(s): Denti, Francesco; Camerlenghi, Federico; Guindani, Michele; Mira, Antonietta
Published in: Journal of the American Statistical Association, Issue 1, 2021, ISSN 1537-274X
Publisher: Taylor and Francis
DOI: 10.6084/m9.figshare.14666073.v1

An information theoretic approach to post randomization methods under differential privacy

Author(s): Fadhel Ayed, Marco Battiston, Federico Camerlenghi
Published in: Statistics and Computing, 2020, ISSN 0960-3174
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s11222-020-09949-3

Deep stable neural networks: Large-width asymptotics and convergence rates

Author(s): Stefano Favaro; Sandra Fortini; Stefano Peluchetti
Published in: Bernoulli, 2023, ISSN 1350-7265
Publisher: Chapman & Hall
DOI: 10.3150/22-bej1553

Rates of convergence in de Finetti’s representation theorem, and Hausdorff moment problem

Author(s): Emanuele Dolera, Stefano Favaro
Published in: Bernoulli, Issue 26/2, 2020, Page(s) 1294-1322, ISSN 1350-7265
Publisher: Chapman & Hall
DOI: 10.3150/19-bej1156

Doubly infinite residual neural networks: a diffusion process approach

Author(s): Stefano Peluchetti, Stefano Favaro
Published in: Journal of Machine Learning Research, Issue 22, 2021, Page(s) 1-48, ISSN 1533-7928
Publisher: MIT Press

Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis

Author(s): Boyu Ren, Sergio Bacallado, Stefano Favaro, Tommi Vatanen, Curtis Huttenhower, Lorenzo Trippa
Published in: The Annals of Applied Statistics, Issue 14/1, 2020, Page(s) 494-517, ISSN 1932-6157
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/19-aoas1295

Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference

Author(s): Emanuele Dolera
Published in: Mathematics, Issue 4, 2022, ISSN 2227-7390
Publisher: MDPI
DOI: 10.3390/math10071136

Consistent estimation of small masses in feature sampling

Author(s): Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Published in: Journal of Machine Learning Research, Issue 22, 2021, ISSN 1533-7928
Publisher: MIT press

Approximating Predictive Probabilities of Gibbs-Type Priors

Author(s): Julyan Arbel, Stefano Favaro
Published in: Sankhya A, 2020, ISSN 0976-836X
Publisher: Indian Statistical Institute
DOI: 10.1007/s13171-019-00187-y

Optimal disclosure risk assessment

Author(s): Federico Camerlenghi, Stefano Favaro, Zacharie Naulet, Francesca Panero
Published in: The Annals of Statistics, 2020, ISSN 0090-5364
Publisher: Institute of Mathematical Statistics

On uniform continuity of posterior distributions

Author(s): Emanuele Dolera, Edoardo Mainini
Published in: Statistics & Probability Letters, Issue 157, 2020, Page(s) 108627, ISSN 0167-7152
Publisher: Elsevier BV
DOI: 10.1016/j.spl.2019.108627

Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation

Author(s): Federico Camerlenghi; Stefano Favaro; Lorenzo Masoero; Tamara Broderick
Published in: Journal of the American Statistical Association, 2024, ISSN 0162-1459
Publisher: American Statistical Association

Strong posterior contraction rates via Wasserstein dynamics

Author(s): Dolera, Emanuele; Favaro, Stefano; Mainini, Edoardo
Published in: Probability Theory and Related Field, Issue 4, 2023, ISSN 0178-8051
Publisher: Springer Verlag

More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

Author(s): Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro, Tamara Broderick
Published in: Biometrika, 2021, ISSN 0006-3444
Publisher: Oxford University Press
DOI: 10.1093/biomet/asab012

A Berry–Esseen theorem for Pitman’s $\alpha $-diversity

Author(s): Emanuele Dolera, Stefano Favaro
Published in: Annals of Applied Probability, Issue 30/2, 2020, Page(s) 847-869, ISSN 1050-5164
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/19-aap1518

Perfect Sampling of the Posterior in the Hierarchical Pitman–Yor Process

Author(s): Sergio Bacallado, Stefano Favaro, Samuel Power, Lorenzo Trippa
Published in: Bayesian Analysis, Issue -1/-1, 2021, ISSN 1936-0975
Publisher: Carnegie Mellon University
DOI: 10.1214/21-ba1269

Conference proceedings (4)

A Bayesian nonparametric approach to count-min sketch under power-law data streams

Author(s): Emanuele Dolera, Stefano Favaro, Stefano Peluchetti
Published in: Proceedings of the Twenty Fourth International Conference on Artificial Intelligence and Statistics, Issue 130, 2021, Page(s) 226-234, ISSN 2640-3498
Publisher: MIT Press

Infinitely deep neural networks as diffusion processes

Author(s): Stefano Peluchetti, Stefano Favaro
Published in: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, Issue 108, 2020, Page(s) 1126-1136
Publisher: MIT Press

Stable behaviour of infinitely wide deep neural networks

Author(s): Stefano Favaro, Sandra Fortini, Stefano Peluchetti
Published in: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, Issue 108, 2020, Page(s) 1137-1146
Publisher: MIT Press

Large-width functional asymptotics for deep Gaussian neural networks

Author(s): Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti
Published in: International Conference on Learning Representations, Issue 9, 2021, Page(s) 1-10
Publisher: OpenReview