AbstractThe conjugate prior for the exponential family, referred to also as the natural conjugate prior, is represented in terms of the Kullback–Leibler separator. This representation permits us to extend the conjugate prior to that for a general family of sampling distributions. Further, by replacing the Kullback–Leibler separator with its dual form, we define another form of a prior, which will be called the mean conjugate prior. Various results on duality between the two conjugate priors are shown. Implications of this approach include richer families of prior distributions induced by a sampling distribution and the empirical Bayes estimation of a high-dimensional mean parameter
Reference analysis is one of the most successful general methods to derive noninformative prior dis...
Bayesian Inference, Conjugate Parameterisation, Enriched Prior, Extended Conjugate Family, Posterior...
The selection of prior distributions is a problem that has been heavily discussed since Bayes and Pr...
AbstractThe conjugate prior for the exponential family, referred to also as the natural conjugate pr...
Given an exponential family of sampling distributions of order k, one may construct in a natural way...
Consider a natural exponential family parameterized by θ. It is well known that the standard conjuga...
Reconsidering generalizations of the original Bayesian framework that have been suggested during the...
The family of proper conjugate priors is characterized in a general exponential model for stochastic...
Abstract. There are several ways to parameterize a distribution belonging to an exponential family, ...
This short note contains an explicit proof of the Dirichlet distribu-tion being the conjugate prior ...
We construct an enrichment of the Dirichlet Process that is more flexible with respect to the precis...
In the paper the family of proper conjugate priors for curved exponential families of stochastic pro...
International audienceThere are several ways to parameterize a distribution belonging to an exponent...
This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior t...
AbstractReference analysis is one of the most successful general methods to derive noninformative pr...
Reference analysis is one of the most successful general methods to derive noninformative prior dis...
Bayesian Inference, Conjugate Parameterisation, Enriched Prior, Extended Conjugate Family, Posterior...
The selection of prior distributions is a problem that has been heavily discussed since Bayes and Pr...
AbstractThe conjugate prior for the exponential family, referred to also as the natural conjugate pr...
Given an exponential family of sampling distributions of order k, one may construct in a natural way...
Consider a natural exponential family parameterized by θ. It is well known that the standard conjuga...
Reconsidering generalizations of the original Bayesian framework that have been suggested during the...
The family of proper conjugate priors is characterized in a general exponential model for stochastic...
Abstract. There are several ways to parameterize a distribution belonging to an exponential family, ...
This short note contains an explicit proof of the Dirichlet distribu-tion being the conjugate prior ...
We construct an enrichment of the Dirichlet Process that is more flexible with respect to the precis...
In the paper the family of proper conjugate priors for curved exponential families of stochastic pro...
International audienceThere are several ways to parameterize a distribution belonging to an exponent...
This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior t...
AbstractReference analysis is one of the most successful general methods to derive noninformative pr...
Reference analysis is one of the most successful general methods to derive noninformative prior dis...
Bayesian Inference, Conjugate Parameterisation, Enriched Prior, Extended Conjugate Family, Posterior...
The selection of prior distributions is a problem that has been heavily discussed since Bayes and Pr...