Background: MR imaging is one of the best diagnostic modalities in medicine. During an MR imaging examination, three types of field are employed to produce images. Various experimental studies have been performed about the effects of each single type of field but only few studies are available on their combination to generate MR imaging. The main objective of this research work is to study the ...

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...

In this paper, the statistical inference of the unknown parameters of a twoparameter inverse Weibull (IW) distribution based on the progressive Type-II censored sample has been considered. The maximum likelihood estimators cannot be obtained in explicit forms, hence the approximate maximum likelihood estimators are proposed, which are in explicit forms. The Bayes and generalized Bayes estimator...

My final project was to implement and compare a number of Naive Bayes and boosting algorithms. For this task I chose to implement two Naive Bayes algorithms that are able to make use of binary attributes, the multivariate Naive Bayes and the multinomial Naive Bayes with binary attributes. For the boosting side of the algorithms I chose to implement AdaBoost, and its close bother AdaBoost*. Both...

In this paper, using empirical Bayes (EB) approach, we construct Bayes estimator and empirical Bayes estimator for the subordinate function of parameter of the Pareto distribution families under the condition that the present sample and the past samples are randomly censored from the right by another variable with an unknown distribution, and discusses Bayes estimate and experience Bayes estima...

Recently two-parameter generalized exponential distribution has been introduced by the authors. In this paper we consider the Bayes estimators of the unknown parameters under the assumptions of gamma priors on both the shape and scale parameters. The Bayes estimators can not be obtained in explicit forms. Approximate Bayes estimators are computed using the idea of Lindley. We also propose Gibbs...

In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Baye...

It is well known that in unidentifiable models, the Bayes estimation has the advantage of generalization performance to the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an empirical Bayes approach where a part of the parameters are regarded as hyperparameters, which we call a subspace B...

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...