In this study, the application of Bayes networks and fault tree analysis in reliability estimation have been investigated. Fault tree analysis is one of the most widely used methods for estimating reliability. In recent years, a method called "Bayes Network" has been used, which is a dynamic method, and information about the probable failure of the system components will be updated according to...

Bayes linear kinematics and graphical models provide an extension of methods so that full conditional updates may be combined with belief adjustment. The use eliminates the problem non-commutativity which was observed in earlier work involving moment-based updates. In this paper we describe approach investigate its application to rapid computation prognostic index values survival when a patient...

collaborative spectrum sensing (css) is an effective approach to improve the detection performance in cognitive radio (cr) networks. inherent characteristics of the cr have imposed some additional security threats to the networks. one of the common threats is primary user emulation attack (puea). in puea, some malicious users try to imitate primary signal characteristics and defraud the cr user...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

genomic selection (gs) is a tool for prediction of breeding values for quantitative traits. for a successful application of gs, accuracy of predicted genomic breeding value (gebv) is a key issue to consider. here we investigated the accuracy of gebv in 345 genotyped iranian holstein cattle. the study was performed on milk, fat, protein yield and somatic cell count. four methods g-blup, bayes b,...

In unidentifiable models, the Bayes estimation has the advantage of generalization performance over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach. A subspace Bayes approach is an empirical Bayes approach whe...

advanced data mining techniques can be used in universities classification, discovering specific patterns in the determination of successful students, design of a plan or a teaching method and finding critical points of financial management. in this article, we proposed a method to predict the rate of student enrollment in coming years. the data for this research were from data sets of voluntee...

Factored Markov Decision Processes is the theoretical framework underlying multi-step Learning Classifier Systems research. This framework is mostly used in the context of Two-stage Bayes Networks, a subset of Bayes Networks. In this paper, we compare the Learning Classifier Systems approach and the Bayes Networks approach to factored Markov Decision Problems. More specifically, we focus on a c...