The Bayesian approach to testing hypotheses has two critical elements. First, a Bayesian approach stands the classical (by which I mean frequentist but I can’t bring myself to type these “ist” designations) approach on its head. In the classical approach, our questions revolve around the probability of the data, given a specific hypothesis. In the Bayesian approach, our questions revolve around...

Let X1, X2, ..., Xr be the first r order statistics from a sample of size n from the generalized exponential distribution with shape parameter θ. In this paper, we consider a Bayesian approach to predicting future order statistics based on the observed ordered data. The predictive densities are obtained and used to determine prediction intervals for unobserved order statistics for one-sample ...

This paper considers the testing of fuzzy hypotheses on the basis of a Bayesian approach. For this, using a notion of prior distribution with interval or fuzzy-valued parameters, we extend a concept of posterior probability of a fuzzy hypothesis. Some of its properties are also put into investigation. The feasibility and effectiveness of the proposed methods are also cla...

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of interest. This paper presents the princ...

background : the age standardized incidence rates of esophageal cancer in women and men in iran have the fifth and eighth ranks, respectively. due to the high incidence rate of esophageal cancer and the necessity of knowledge of its geographical distribution, the incidence of this cancer was mapped at province and county levels in the present study. methods: the study population of this empiric...

Bayesian learning has been widely used and proved to be effective in many data modeling problems. However, computations involved in it require huge costs and generally cannot be performed exactly. The variational Bayesian approach, proposed as an approximation of Bayesian learning, has provided computational tractability and good generalization performance in many applications. The properties a...