Reconsidering generalizations of the original Bayesian framework that have been suggested during the last three decades, imprecise conjugate prior densities are proposed for members of the one-parameter exponential family of distributions
Conjugate prior distributions, Finite mixture of distributions, Kullback–Leibler divergence, Natural...
Bayesian inference enables combination of observations with prior knowledge in the reasoning process...
A great advantage of imprecise probability models over models based on precise, traditional probabil...
Reconsidering generalizations of the original Bayesian framework that have been suggested during the...
Given an exponential family of sampling distributions of order k, one may construct in a natural way...
AbstractThe conjugate prior for the exponential family, referred to also as the natural conjugate pr...
Abstract. There are several ways to parameterize a distribution belonging to an exponential family, ...
Bayesian Inference, Conjugate Parameterisation, Enriched Prior, Extended Conjugate Family, Posterior...
The fiducial argument was introduced by Fisher in order to obtain distributions for unknown paramet...
International audienceThere are several ways to parameterize a distribution belonging to an exponent...
When considering sampling models described by a distribution from an exponential family, it is possi...
Consider a natural exponential family parameterized by θ. It is well known that the standard conjuga...
This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior t...
The family of proper conjugate priors is characterized in a general exponential model for stochastic...
Consider a standard conjugate family of prior distributions for a vectorparameter indexing an expone...
Conjugate prior distributions, Finite mixture of distributions, Kullback–Leibler divergence, Natural...
Bayesian inference enables combination of observations with prior knowledge in the reasoning process...
A great advantage of imprecise probability models over models based on precise, traditional probabil...
Reconsidering generalizations of the original Bayesian framework that have been suggested during the...
Given an exponential family of sampling distributions of order k, one may construct in a natural way...
AbstractThe conjugate prior for the exponential family, referred to also as the natural conjugate pr...
Abstract. There are several ways to parameterize a distribution belonging to an exponential family, ...
Bayesian Inference, Conjugate Parameterisation, Enriched Prior, Extended Conjugate Family, Posterior...
The fiducial argument was introduced by Fisher in order to obtain distributions for unknown paramet...
International audienceThere are several ways to parameterize a distribution belonging to an exponent...
When considering sampling models described by a distribution from an exponential family, it is possi...
Consider a natural exponential family parameterized by θ. It is well known that the standard conjuga...
This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior t...
The family of proper conjugate priors is characterized in a general exponential model for stochastic...
Consider a standard conjugate family of prior distributions for a vectorparameter indexing an expone...
Conjugate prior distributions, Finite mixture of distributions, Kullback–Leibler divergence, Natural...
Bayesian inference enables combination of observations with prior knowledge in the reasoning process...
A great advantage of imprecise probability models over models based on precise, traditional probabil...
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