We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a non-asymptotic method that can be used to quantify the evidence for or against a sub-model. We give expressions for the generalized fractional Bayes factor and we study its properties. In particular, we show that the generalized fractional Bayes factor has better properties than the fr...

Abstract Bayes estimators of the scale parameter of a generalized gamma type model are obtained for different priors. For the proposed prior, Bayes estimator of θ for given p and k=1 coincides with MLE of θ if c=2, whereas Bayes estimator of hazard rate function of θ given p and k coincides with its MLE. Thumb’s rule has been used for constructing a conjugate prior for θ . Bayes estimators of r...

using a sample fiom burr type xu distribution, bayes prediction intervals are derived for the maximum and minimum of a future sample fromthe same distribution, but in the presence of a single outlier of the type 8,8. the prior of q is assumed to be the gamma conjugate. a real example is given to illustrate the procedure. also, the comparison between the values of the prediction bounds for diffe...

A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...

The naïve Bayes classifier has been widely applied to decisionmaking or classification. Because the naïve Bayes classifier prefers to dealing with discrete values, an novel discretization approach is proposed to improve naïve Bayes classifier and enhance decision accuracy in this paper. Based on the statistical information of the naïve Bayes classifier, a distributional index is defined in the ...

This paper considers inference under progressive type II censoring with a compound Rayleigh failure time distribution. The maximum likelihood (ML), and Bayes methods are used for estimating the unknown parameters as well as some lifetime parameters, namely reliability and hazard functions. We obtained Bayes estimators using the conjugate priors for two shape and scale parameters. When the two p...

The problem of state estimation with stochastic uncertainties in the initial state, model noise, and measurement noise is approached using the restricted risk Bayes approach. It is assumed that the a priori distributions of these quantities are not perfectly known but that some a priori information may be available. While offering robustness, the restricted risk Bayes approach incorporates the ...

The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness—the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser d...