This paper presents an extension of Bayesian networks (BN) applied to reliability analysis. We developed a general methodology for modelling reliability of complex systems based on Bayesian networks. A reliability structure represented as a reliability block diagram is transformed to a Bayesian network representation, and with this, the reliability of the system can be obtained using probabilit...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

An adaptive on-line questionnaire, named EDUFORM, is based on Bayesian statistical techniques that both optimize the number of propositions presented to each respondent and create an individual learner profile. Adaptive graphical user interface is generated partially (propositions of the questionnaire, collaborative actions and links to resources) and computational part totally with Bayesian co...

Cancer detection is one of the important research topics in medical science. In bioinformatics age, gene expression data can be used for the cancer detection. Data mining techniques, such as pattern association, classification and clustering, are now frequently applied in cancer and gene expressions correlation studies. Classification is very important among these techniques of data mining. Her...

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect f...

To support and guide an extensive experimental research into systems biology of signaling pathways, increasingly more mechanistic models are being developed with hopes of gaining further insight into biological processes. In order to analyze these models, computational and statistical techniques are needed to estimate the unknown kinetic parameters. This chapter reviews methods from frequentist...

An enhanced power-line communications channel estimation method in discrete multi-tone (DMT) communication system based on sparse Bayesian regression is presented. By exploiting a probabilistic Bayesian learning framework, the sparse model used provides an accurate model for channel estimation in presence of noise and consequently equalization. We consider frequency domain equalization (FEQ) us...

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework to implement ...

The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The m...

This thesis is a comprehensive comparative study of survival analysis methods, in particular the application of the Cox Proportional Hazards (CPH) model to real life data: A data set with 48 right-censored (end of study) patients suffering from multiple myeloma, and the COpenhagen Stroke study (COST) database with 993 right-censored (10 year follow-up) stroke patients. The most frequently appli...