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

Publicaciones

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

Autores: Federico Camerlenghi, Stefano Favaro
Publicado en: Mathematics, 2021, ISSN 2227-7390
Editor: MDPI
DOI: 10.3390/math9222891

Upscaling human activity data: A statistical ecology approach

Autores: Anna Tovo, Samuele Stivanello, Amos Maritan, Samir Suweis, Stefano Favaro, Marco Formentin
Publicado en: PLOS ONE, Edición 16/7, 2021, Página(s) e0253461, ISSN 1932-6203
Editor: Public Library of Science
DOI: 10.1371/journal.pone.0253461

Bayesian nonparametric disclosure risk assessment

Autores: Stefano Favaro, Francesca Panero, Tommaso Rigon
Publicado en: Electronic Journal of Statistics, 2021, ISSN 1935-7524
Editor: Institute of Mathematical Statistics
DOI: 10.1214/21-ejs1933

A Compound Poisson Perspective of Ewens–Pitman Sampling Model

Autores: Emanuele Dolera, Stefano Favaro
Publicado en: Mathematics, 2021, ISSN 2227-7390
Editor: MDPI
DOI: 10.3390/math9212820

On consistent and rate optimal estimation of the missing mass

Autores: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Publicado en: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, Edición 57/3, 2021, Página(s) 1476-1494, ISSN 0246-0203
Editor: Elsevier BV
DOI: 10.1214/20-aihp1126

A Good-Turing estimator for feature allocation models

Autores: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Publicado en: Electronic Journal of Statistics, Edición 13/2, 2019, Página(s) 3775-3804, ISSN 1935-7524
Editor: Institute of Mathematical Statistics
DOI: 10.1214/19-ejs1614

Nonparametric Bayesian multiarmed bandits for single-cell experiment design

Autores: Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro
Publicado en: The Annals of Applied Statistics, Edición 14/4, 2020, Página(s) 2003-2019, ISSN 1932-6157
Editor: Institute of Mathematical Statistics
DOI: 10.1214/20-aoas1370

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

Autores: Denti, Francesco; Camerlenghi, Federico; Guindani, Michele; Mira, Antonietta
Publicado en: Journal of the American Statistical Association, Edición 1, 2021, ISSN 1537-274X
Editor: Taylor and Francis
DOI: 10.6084/m9.figshare.14666073.v1

An information theoretic approach to post randomization methods under differential privacy

Autores: Fadhel Ayed, Marco Battiston, Federico Camerlenghi
Publicado en: Statistics and Computing, 2020, ISSN 0960-3174
Editor: Kluwer Academic Publishers
DOI: 10.1007/s11222-020-09949-3

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

Autores: Stefano Favaro; Sandra Fortini; Stefano Peluchetti
Publicado en: Bernoulli, 2023, ISSN 1350-7265
Editor: Chapman & Hall
DOI: 10.3150/22-bej1553

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

Autores: Emanuele Dolera, Stefano Favaro
Publicado en: Bernoulli, Edición 26/2, 2020, Página(s) 1294-1322, ISSN 1350-7265
Editor: Chapman & Hall
DOI: 10.3150/19-bej1156

Doubly infinite residual neural networks: a diffusion process approach

Autores: Stefano Peluchetti, Stefano Favaro
Publicado en: Journal of Machine Learning Research, Edición 22, 2021, Página(s) 1-48, ISSN 1533-7928
Editor: MIT Press

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

Autores: Boyu Ren, Sergio Bacallado, Stefano Favaro, Tommi Vatanen, Curtis Huttenhower, Lorenzo Trippa
Publicado en: The Annals of Applied Statistics, Edición 14/1, 2020, Página(s) 494-517, ISSN 1932-6157
Editor: Institute of Mathematical Statistics
DOI: 10.1214/19-aoas1295

Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference

Autores: Emanuele Dolera
Publicado en: Mathematics, Edición 4, 2022, ISSN 2227-7390
Editor: MDPI
DOI: 10.3390/math10071136

Consistent estimation of small masses in feature sampling

Autores: Fadhel Ayed, Marco Battiston, Federico Camerlenghi, Stefano Favaro
Publicado en: Journal of Machine Learning Research, Edición 22, 2021, ISSN 1533-7928
Editor: MIT press

Approximating Predictive Probabilities of Gibbs-Type Priors

Autores: Julyan Arbel, Stefano Favaro
Publicado en: Sankhya A, 2020, ISSN 0976-836X
Editor: Indian Statistical Institute
DOI: 10.1007/s13171-019-00187-y

Optimal disclosure risk assessment

Autores: Federico Camerlenghi, Stefano Favaro, Zacharie Naulet, Francesca Panero
Publicado en: The Annals of Statistics, 2020, ISSN 0090-5364
Editor: Institute of Mathematical Statistics

On uniform continuity of posterior distributions

Autores: Emanuele Dolera, Edoardo Mainini
Publicado en: Statistics & Probability Letters, Edición 157, 2020, Página(s) 108627, ISSN 0167-7152
Editor: Elsevier BV
DOI: 10.1016/j.spl.2019.108627

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

Autores: Federico Camerlenghi; Stefano Favaro; Lorenzo Masoero; Tamara Broderick
Publicado en: Journal of the American Statistical Association, 2024, ISSN 0162-1459
Editor: American Statistical Association

Strong posterior contraction rates via Wasserstein dynamics

Autores: Dolera, Emanuele; Favaro, Stefano; Mainini, Edoardo
Publicado en: Probability Theory and Related Field, Edición 4, 2023, ISSN 0178-8051
Editor: Springer Verlag

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

Autores: Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro, Tamara Broderick
Publicado en: Biometrika, 2021, ISSN 0006-3444
Editor: Oxford University Press
DOI: 10.1093/biomet/asab012

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

Autores: Emanuele Dolera, Stefano Favaro
Publicado en: Annals of Applied Probability, Edición 30/2, 2020, Página(s) 847-869, ISSN 1050-5164
Editor: Institute of Mathematical Statistics
DOI: 10.1214/19-aap1518

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

Autores: Sergio Bacallado, Stefano Favaro, Samuel Power, Lorenzo Trippa
Publicado en: Bayesian Analysis, Edición -1/-1, 2021, ISSN 1936-0975
Editor: Carnegie Mellon University
DOI: 10.1214/21-ba1269

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

Autores: Emanuele Dolera, Stefano Favaro, Stefano Peluchetti
Publicado en: Proceedings of the Twenty Fourth International Conference on Artificial Intelligence and Statistics, Edición 130, 2021, Página(s) 226-234, ISSN 2640-3498
Editor: MIT Press

Infinitely deep neural networks as diffusion processes

Autores: Stefano Peluchetti, Stefano Favaro
Publicado en: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, Edición 108, 2020, Página(s) 1126-1136
Editor: MIT Press

Stable behaviour of infinitely wide deep neural networks

Autores: Stefano Favaro, Sandra Fortini, Stefano Peluchetti
Publicado en: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, Edición 108, 2020, Página(s) 1137-1146
Editor: MIT Press

Large-width functional asymptotics for deep Gaussian neural networks

Autores: Daniele Bracale, Stefano Favaro, Sandra Fortini, Stefano Peluchetti
Publicado en: International Conference on Learning Representations, Edición 9, 2021, Página(s) 1-10
Editor: OpenReview

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