Random Bernstein polynomials induces a probability measure on the space of multivariate density functions on a unit cube. For density estimation, it is important that the Bernstein prior can be restricted to an admissible class of densities with certain geometric properties of the target density. In this article, we study the shape properties such as monotonicity, convexity, and symmetry of the Bernstein prior.Bernstein prior Convexity Monotonicity Symmetry
In this paper, we show that several integral operators, which are associated with beta-type probabil...
A common concern with Bayesian analysis is uncertainty in specification of the prior distribution. T...
We study the asymptotic properties of the Bernstein estimator for unbounded density copula function...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
This paper considers multivariate extension of smooth estimator of the distribution and density func...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
Our goal is inference for shape-restricted functions. Our functional form consists of finite linear ...
Bayesian predictive densities for the 2-dimensional Wishart model are investigated. The performance ...
AbstractThis paper discusses the criteria of convexity, monotonicity, and positivity of Bernstein-Bé...
International audienceDespite its slow convergence, the use of the Bernstein polynomial approximatio...
In this paper, we show that several integral operators, which are associated with beta-type probabil...
A common concern with Bayesian analysis is uncertainty in specification of the prior distribution. T...
We study the asymptotic properties of the Bernstein estimator for unbounded density copula function...
Our focus is on constructing a multiscale nonparametric prior for densities. The Bayes density estim...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
This paper considers multivariate extension of smooth estimator of the distribution and density func...
This paper introduces a new approach to Bayesian nonparametric inference for densities on the hyper...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We propose a Bayesian nonparametric procedure for density estimation, for data in a closed, bounded ...
Random Bernstein polynomials which are also probability distribution functions on the closed unit in...
Our goal is inference for shape-restricted functions. Our functional form consists of finite linear ...
Bayesian predictive densities for the 2-dimensional Wishart model are investigated. The performance ...
AbstractThis paper discusses the criteria of convexity, monotonicity, and positivity of Bernstein-Bé...
International audienceDespite its slow convergence, the use of the Bernstein polynomial approximatio...
In this paper, we show that several integral operators, which are associated with beta-type probabil...
A common concern with Bayesian analysis is uncertainty in specification of the prior distribution. T...
We study the asymptotic properties of the Bernstein estimator for unbounded density copula function...