In this paper we derive adaptive non-parametric rates of concentration of the posterior distributions for the density model on the class of Sobolev and Besov spaces. For this purpose, we build prior models based on wavelet or Fourier expansions of the logarithm of the density. The prior models are not necessarily Gaussianou
Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two r...
We derive rates of contraction of posterior distributions on nonparametric models resulting from sie...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
International audienceIn this paper we derive adaptive non-parametric rates of concentration of the ...
The problem of estimating probability densities on the unit interval whose log-functions belong to a...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
In this paper, we investigate the asymptotic properties of nonparametric Bayesian mixtures of Betas ...
In this paper, we consider the well known problem of estimating a density function under qualitative...
In this work we investigate the asymptotic properties of nonparametric bayesian mixtures of Betas fo...
We provide general conditions to derive posterior concentration rates for Aalen counting processes. ...
In this paper some prior distributions for densities in infinitedimensional exponential families, wh...
We provide sufficient conditions to derive posterior concentration rates for Aalen counting processe...
We investigate the asymptotic properties of posterior distributions when the model is misspecified, ...
We study convergence rates of Bayesian density estimators based on finite location-scale mixtures of...
Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two r...
We derive rates of contraction of posterior distributions on nonparametric models resulting from sie...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
International audienceIn this paper we derive adaptive non-parametric rates of concentration of the ...
The problem of estimating probability densities on the unit interval whose log-functions belong to a...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
We study the rate of convergence of posterior distributions in density estimation problems for log-d...
In this paper, we investigate the asymptotic properties of nonparametric Bayesian mixtures of Betas ...
In this paper, we consider the well known problem of estimating a density function under qualitative...
In this work we investigate the asymptotic properties of nonparametric bayesian mixtures of Betas fo...
We provide general conditions to derive posterior concentration rates for Aalen counting processes. ...
In this paper some prior distributions for densities in infinitedimensional exponential families, wh...
We provide sufficient conditions to derive posterior concentration rates for Aalen counting processe...
We investigate the asymptotic properties of posterior distributions when the model is misspecified, ...
We study convergence rates of Bayesian density estimators based on finite location-scale mixtures of...
Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two r...
We derive rates of contraction of posterior distributions on nonparametric models resulting from sie...
We provide conditions on the statistical model and the prior probability law to derive contraction r...
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