This article introduces a Bayesian methodology for the prediction for computer experiments having quantitative and qualitative inputs. The proposed model is a hierarchical Bayesian model with conditional Gaussian stochastic process components. For each of the qualitative inputs, our model assumes that the outputs corresponding to different levels of the qualitative input have “similar” function...

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data. In PCA of a mixture of quantitative and qualitative data, it requires quantification of qualitative data to obtain optimal scaling data and use ordinary PCA. The extended PCA including such quantification is called nonlinear PCA, see Gifi [Gifi, 1990]. The existing algorithms for non...

Risk assessments of big and complex IT infrastructures comprise numerous qualitative risk estimations for infrastructure assets. Qualitative risk estimations, however, are subjective and thus prone to errors. We present an approach to detect anomalies in the result of risk assessments by considering information about inter-dependencies between various building blocks of IT landscapes from enter...

Introduction: Due to the significance of desirable organizational behaviors, all available resources have been utilized improve behavior training. Aim: The purpose this qualitative research is provide a model for enhancing training based on cognitive-behavioral components. Method: current was mixed in terms data collection and applied purpose. According Strauss Corbin's model, method employed g...

Many chemistry students have difficulty in understanding an organic chemistry subject called reaction mechanisms. Mastering the subject would require the application of chemical intuition and chemical commonsense adequately. This work discusses a novel framework using Qualitative Reasoning (QR) to provide means for learning reaction mechanisms through simulation. The framework consists of a num...