Add to ORKGWe consider the problem of Bayesian density deconvolution, when the mixing density is modelled as a Dirichlet-Laplace mixture. We give an assessment of the posterior accuracy in recovering the true mixing density, when it is itself a Laplace mixture. The results partially complement those in Donnet et al. (2014) and an application of them to empirical Bayes density deconvolution can be envisaged
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
Bayesian nonparametric methods have recently gained popularity in the context of density estimation....
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
We consider the problem of recovering a distribution function on the real line from observations add...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
This chapter addresses the problem of recovering the mixing distribution in finite kernel mixture mo...
We study the reknown deconvolution problem of recovering a distribution function from independent re...
<div><p>We consider the problem of estimating the density of a random variable when precise measurem...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
this paper, we settle this issue in affirmative. Running Head. Consistency of Dirichlet mixtures
We consider Bayesian nonparametric density estimation with a Dirichlet process kernel mixture as a ...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
Bayesian nonparametric methods have recently gained popularity in the context of density estimation....
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
We consider the problem of recovering a distribution function on the real line from observations add...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
In this paper we provide general conditions to check on the model and the prior to derive posterior ...
This chapter addresses the problem of recovering the mixing distribution in finite kernel mixture mo...
We study the reknown deconvolution problem of recovering a distribution function from independent re...
<div><p>We consider the problem of estimating the density of a random variable when precise measurem...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
this paper, we settle this issue in affirmative. Running Head. Consistency of Dirichlet mixtures
We consider Bayesian nonparametric density estimation with a Dirichlet process kernel mixture as a ...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
Bayesian nonparametric methods have recently gained popularity in the context of density estimation....
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...