This paper contributes to the theory of Bayesian consistency for a sequence of posterior and predictive distributions arising from an independent and identically distributed sample. A new sufficient condition for posterior Hellinger consistency is presented which provides motivation for recent results appearing in the literature. Such motivation is important since current sufficient conditions are not known to be necessary. It also provides new insights into Bayesian consistency. A new consistency theorem for the sequence of predictive densities is given
We extend Doob's well-known result on Bayesian consistency. The extension covers the case where the ...
We consider sufficient conditions for Bayesian consistency of the transition density of time homogen...
We extend Doob's well-known result on Bayesian consistency The extension covers the case where the n...
We consider a sequence of posterior distributions based on a data-dependent prior (which we shall re...
In this paper, we provide a Doob-style consistency theorem for stationary models. Many applications ...
We introduce the Hausdorff α-entropy to study the strong Hellinger con-sistency of posterior distrib...
In this paper, we provide a Doob-style consistency theorem for stationary models. Many applications ...
We establish a sufficient condition ensuring strong Hellinger consistency of posterior distributions...
Bayesian consistency is an important issue in the context of non- parametric problems. The posterior...
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is describe...
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is describe...
We extend Doob’s well-known result on Bayesian consistency. The extension covers the case where the ...
Posterior consistency can be thought of as a theoretical justification of the Bayesian method. One o...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We consider sufficient conditions for Bayesian consistency of the transition density of time homogen...
We extend Doob's well-known result on Bayesian consistency. The extension covers the case where the ...
We consider sufficient conditions for Bayesian consistency of the transition density of time homogen...
We extend Doob's well-known result on Bayesian consistency The extension covers the case where the n...
We consider a sequence of posterior distributions based on a data-dependent prior (which we shall re...
In this paper, we provide a Doob-style consistency theorem for stationary models. Many applications ...
We introduce the Hausdorff α-entropy to study the strong Hellinger con-sistency of posterior distrib...
In this paper, we provide a Doob-style consistency theorem for stationary models. Many applications ...
We establish a sufficient condition ensuring strong Hellinger consistency of posterior distributions...
Bayesian consistency is an important issue in the context of non- parametric problems. The posterior...
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is describe...
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is describe...
We extend Doob’s well-known result on Bayesian consistency. The extension covers the case where the ...
Posterior consistency can be thought of as a theoretical justification of the Bayesian method. One o...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
We consider sufficient conditions for Bayesian consistency of the transition density of time homogen...
We extend Doob's well-known result on Bayesian consistency. The extension covers the case where the ...
We consider sufficient conditions for Bayesian consistency of the transition density of time homogen...
We extend Doob's well-known result on Bayesian consistency The extension covers the case where the n...