Add to ORKGThis paper introduces a new approach to Bayesian nonparametric inference for densities on the hypercube, based on the use of a multivariate Bernstein polynomial prior. Posterior convergence rates under the proposed prior are obtained. Furthermore, a novel sampling scheme, based on the use of slice sampling techniques, is proposed for estimation of the posterior predictive density. The approach is illustrated with both simulated and real data example
Traditionally statisticians thought that nonparametrically estimating quantities such as density fun...
In the first paper, we propose a flexible class of priors for density estimation avoiding discrete m...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
This dissertation focuses on the frequentist properties of Bayesian procedures in a broad spectrum o...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We consider Bayesian density estimation for compactly supported densities using Bernstein mixtures o...
For binomial data analysis, many methods based on empirical Bayes interpretations have been develope...
This paper considers multivariate extension of smooth estimator of the distribution and density func...
In this thesis, we propose a new nonparametric approach based on Bernstein polynomials to estimate t...
msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric infer...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
International audienceDespite its slow convergence, the use of the Bernstein polynomial approximatio...
Traditionally statisticians thought that nonparametrically estimating quantities such as density fun...
In the first paper, we propose a flexible class of priors for density estimation avoiding discrete m...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
This dissertation focuses on the frequentist properties of Bayesian procedures in a broad spectrum o...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We consider Bayesian density estimation for compactly supported densities using Bernstein mixtures o...
For binomial data analysis, many methods based on empirical Bayes interpretations have been develope...
This paper considers multivariate extension of smooth estimator of the distribution and density func...
In this thesis, we propose a new nonparametric approach based on Bernstein polynomials to estimate t...
msBP is an R package that implements a new method to perform Bayesian multiscale nonparametric infer...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
International audienceDespite its slow convergence, the use of the Bernstein polynomial approximatio...
Traditionally statisticians thought that nonparametrically estimating quantities such as density fun...
In the first paper, we propose a flexible class of priors for density estimation avoiding discrete m...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...