Noninformative priors for the common shape parameter of several inverse Gaussian distributions

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...

On the common mean of several inverse Gaussian distributions based on a higher order likelihood method

bayesian estimation of shift point in shape parameter of inverse gaussian distribution under different loss functions

in this paper, a bayesian approach is proposed for shift point detection in an inverse gaussian distribution. in this study, the mean parameter of inverse gaussian distribution is assumed to be constant and shift points in shape parameter is considered. first the posterior distribution of shape parameter is obtained. then the bayes estimators are derived under a class of priors and using variou...

Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions

We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that permit the construction of parameter priors for a large number of DAG models from a small set of assessments. We then present a method for directly computing the marginal likelihood of every DAG model given a random samp...

Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions

We show that the only parameter prior for complete Gaussian DAG models that satis­ fies global parameter independence, complete model equivalence, and some weak regular­ ity assumptions, is the normal-Wishart dis­ tribution. Our analysis is based on the fol­ lowing new characterization of the Wishart distribution: let W be an n x n, n 2: 3, positive-definite symmetric matrix of ran­ dom variabl...