Add to ORKGthis paper, we settle this issue in affirmative. Running Head. Consistency of Dirichlet mixtures
The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of...
We establish that the Dirichlet location scale mixture of normal priors and the logistic Gaussian pr...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
A Dirichlet mixture of normal densities is a useful choice for a prior distribution on densities in ...
Density estimation, especially multivariate density estimation, is a fundamental problem in nonparam...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
We study the rates of convergence of the posterior distribution for Bayesian density estimation with...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
In this paper, we consider the well known problem of estimating a density function under qualitative...
AbstractDensity estimation, especially multivariate density estimation, is a fundamental problem in ...
A Dirichlet mixture of exponential power distributions, as a prior on densities supported on the rea...
In this paper convergence rates of posterior distributions of Ulrich-let mixtures of normal densitie...
Mixtures of Dirichlet process priors offer a reasonable compromise between purely parametric and pur...
In this paper a sequence ot distributions on the set of all probability measures absolutely continuo...
We consider the problem of Bayesian density deconvolution, when the mixing density is modelled as a ...
The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of...
We establish that the Dirichlet location scale mixture of normal priors and the logistic Gaussian pr...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...
A Dirichlet mixture of normal densities is a useful choice for a prior distribution on densities in ...
Density estimation, especially multivariate density estimation, is a fundamental problem in nonparam...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
We study the rates of convergence of the posterior distribution for Bayesian density estimation with...
The past decade has seen a remarkable development in the area of Bayesian nonparametric inference fr...
In this paper, we consider the well known problem of estimating a density function under qualitative...
AbstractDensity estimation, especially multivariate density estimation, is a fundamental problem in ...
A Dirichlet mixture of exponential power distributions, as a prior on densities supported on the rea...
In this paper convergence rates of posterior distributions of Ulrich-let mixtures of normal densitie...
Mixtures of Dirichlet process priors offer a reasonable compromise between purely parametric and pur...
In this paper a sequence ot distributions on the set of all probability measures absolutely continuo...
We consider the problem of Bayesian density deconvolution, when the mixing density is modelled as a ...
The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of...
We establish that the Dirichlet location scale mixture of normal priors and the logistic Gaussian pr...
We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dir...