The Jeffreys noninformative prior distribution for a single unknown parameter is the distribution corresponding to a uniform distribution in the transformed model where the unknown parameter is approximately a location parameter. To obtain a prior distribution with a specified mean but with diffusion reflecting great uncertainty, a natural generalization of the noninformative prior is the distribution corresponding to the constrained maximum entropy distribution in the transformed model. Examples are given
A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assum...
For an Euclidean groupG acting freely on the parameter space, we derive, among several noninformativ...
Hellinger information as a local characteristic of parametric distribution families was first introd...
In a given problem, the Bayesian statistical paradigm requires the specification of a prior distribu...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
A salient feature of Bayesian inference is its ability to incorporate information from a variety of ...
Non-informative priors play crucial role in objective Bayesian analysis. Most popular ways of constr...
Introduction Central in Bayesian statistics is Bayes' theorem, which can be written as follows...
If p is an unknown probability parameter, prior ignorance of its value is appropriately expressed by...
We propose a generalization of the one-dimensional Jeffreys’ rule in order to obtain non informativ...
e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probab...
In this paper, we develop the noninformative priors for the ratio of the scale parameters in the inv...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
We explore using a Gamma distribution - a maximum entropy distribution - as a prior in a Bayesian op...
A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assum...
For an Euclidean groupG acting freely on the parameter space, we derive, among several noninformativ...
Hellinger information as a local characteristic of parametric distribution families was first introd...
In a given problem, the Bayesian statistical paradigm requires the specification of a prior distribu...
The problem of finding a non-informative prior distribution for a parameter is approached using the ...
A salient feature of Bayesian inference is its ability to incorporate information from a variety of ...
Non-informative priors play crucial role in objective Bayesian analysis. Most popular ways of constr...
Introduction Central in Bayesian statistics is Bayes' theorem, which can be written as follows...
If p is an unknown probability parameter, prior ignorance of its value is appropriately expressed by...
We propose a generalization of the one-dimensional Jeffreys’ rule in order to obtain non informativ...
e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probab...
In this paper, we develop the noninformative priors for the ratio of the scale parameters in the inv...
Prior specification for nonparametric Bayesian inference involves the difficult task of quan-tifying...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
We explore using a Gamma distribution - a maximum entropy distribution - as a prior in a Bayesian op...
A fundamental problem in Bayesian nonparametrics consists of selecting a prior distribution by assum...
For an Euclidean groupG acting freely on the parameter space, we derive, among several noninformativ...
Hellinger information as a local characteristic of parametric distribution families was first introd...