Empirical Bayes procedures are commonly used based on the supposedasymptotic equivalence with fully Bayesian procedures, which, however, has not sofar received full theoretical support in terms of uncertainty quantification. In thisnote, we provide some results on contraction rates of empirical Bayes posteriordistributions which are illustrated in nonparametric curve estimation using Dirichletprocess mixture models.Le procedure bayesiane empiriche sono comunemente utilizzate sulla base di una presunta equivalenza asintotica con quelle propriamente bayesiane, la quale, tuttavia, non ha finora ricevuto piena giustificazione teorica in termini di quantificazione dell’incertezza. In questa nota si forniscono alcuni risultati sulla velocità ...
We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observa...
Supplementary material for "Posterior concentration rates for empirical Bayes procedures, with appli...
In this work we consider estimating densities that are location or location-scale mixtures of kernel...
Empirical Bayes procedures are commonly used based on the supposed asymptotic equivalence with fully...
Empirical Bayes procedures are commonly used based on the supposed asymptotic equivalence with fully...
This paper introduces a new approach to the study of rates of convergence for posterior distribution...
Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Proces
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two r...
Empirical Bayes estimation, multiple linear regression model, convergence rates,
A Dirichlet mixture of exponential power distributions, as a prior on densities supported on the rea...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
This manuscript presents a synthesis of my research work over the last few years. It discusses my co...
We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observa...
Supplementary material for "Posterior concentration rates for empirical Bayes procedures, with appli...
In this work we consider estimating densities that are location or location-scale mixtures of kernel...
Empirical Bayes procedures are commonly used based on the supposed asymptotic equivalence with fully...
Empirical Bayes procedures are commonly used based on the supposed asymptotic equivalence with fully...
This paper introduces a new approach to the study of rates of convergence for posterior distribution...
Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Proces
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two r...
Empirical Bayes estimation, multiple linear regression model, convergence rates,
A Dirichlet mixture of exponential power distributions, as a prior on densities supported on the rea...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
This manuscript presents a synthesis of my research work over the last few years. It discusses my co...
We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observa...
Supplementary material for "Posterior concentration rates for empirical Bayes procedures, with appli...
In this work we consider estimating densities that are location or location-scale mixtures of kernel...