This study focuses on estimating the reliability of a multicomponent stress-strength model using two Bayesian approaches‎: ‎E-Bayesian and Hierarchical Bayesian‎. ‎This model follows Inverse Rayleigh distributions with distinct parameters‎. ‎Additionally‎, ‎the efficiency of the proposed methods is compared by employing Monte Carlo simulation and analyzing a data set.

Background: Adolescence is one of the most important periods in the course of human evolution and the prevalence of mental disorders among adolescence in different regions of Iran, especially in southern Iran.Objectives: This study was conducted to determine the prevalence of mental disorders among high school students in Bushehr province, south of Iran.Methods: In this cross-sectional study, 286 high school students were recruited by a multi-stage random sampling in Bushehr province in 2015. A general health questionnaire (GHQ-28) was used to assess mental disorders. The small area method, under the Hierarchical Bayesian approach, was used to determine the prevalence of mental disorders and data analysis.Results: From 286 questionnaires only 182 were completely filed and evaluated (the response rate was 70.5%). Of the students, 58.79% and 41.21% were male and female, respectively. Of all students, the prevalence of mental disorders in Bushehr, Dayyer, Deylam, Kangan, Dashtestan, Tangestan, Genaveh, and Dashty were 0.48, 0.42, 0.45, 0.52, 0.41, 0.47, 0.42, and 0.43, respectively.Conclusions: Based on this study, the prevalence of mental disorders among adolescents was increasing in Bushehr Province counties. The lack of a national policy in this way is a serious obstacle to mental health and wellbeing access.

‎The recent advancements in technology have faced an increase in the growth rate of data‎.‎According to the amount of data generated‎, ‎ensuring effective analysis using traditional approaches becomes very complicated‎.‎One of the methods of managing and analyzing big data is classification‎.‎%One of the data mining methods used commonly and effectively to classify big data is the MapReduce‎‎In this paper‎, ‎the feature weighting technique to improve Bayesian classification algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model‎.‎%Classification models include Naive Bayes classifier‎, ‎correlated Naive Bayes and correlated Naive Bayes with feature weighting‎.‎Correlated Naive Bayes classification is a generalization of the Naive Bayes classification model by considering the dependence between features‎.‎%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes classification‎.‎The performance of all described methods are evaluated by considering accuracy‎, ‎sensitivity and specificity‎, ‎accuracy‎, ‎sensitivity and specificity metrics.

This article examines statistical inference for R= P(Y<X) where X and Y are independent but not identically distributed Pareto of the first kind (Pareto (I)) random variables with same scale parameter but different shape parameters. The Maximum likelihood, uniformly minimum variance unbiased and Bayes estimators with Gamma prior are used for this purpose. Simulation studies which compare the estimators are presented. Moreover, sensitivity of Bayes estimator to the prior parameters is considered.