6 papers
Merging of Bayes and quasi-Bayes empirical Bayes procedures for Poisson compound decisions
Stefano Favaro, Sandra Fortini
The Poisson compound decision problem is a long-standing problem in statistics, in which empirical Bayes methods are used to estimate Poisson means under a mixture model. We study…
Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
Stefano Favaro, Sandra Fortini
The Poisson compound decision problem is a long-standing problem is statistics, for which empirical Bayes methods are commonly used to estimate Poisson means in static or batch set…
Elements of Conformal Prediction for Statisticians
Matteo Sesia, Stefano Favaro
Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn…
Function-Space MCMC for Bayesian Wide Neural Networks
Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti
Bayesian Neural Networks represent a fascinating confluence of deep learning and probabilistic reasoning, offering a compelling framework for understanding uncertainty in complex p…
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Francesco Caporali, Stefano Favaro, Dario Trevisan
The asymptotic properties of Bayesian Neural Networks (BNNs) have been extensively studied, particularly regarding their approximations by Gaussian processes in the infinite-width…
Quasi-Bayesian sequential deconvolution
Stefano Favaro, Sandra Fortini
Density deconvolution is the inverse problem of estimating a probability density from observations contaminated by additive noise. Traditionally studied in static or batch settings…