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The null hypothesis in this setting models the normal behavior, while the alternative hypothesis models the abnormal or anomalous behavior.
As one of the most important cloud models, the normal cloud models, especially the 2nd-order normal cloud model based on normal distribution and Gaussian membership function has been extensively researched and successfully applied to many fields.
In current risk management practice there are three basic types of VaR models, the normal linear VaR model (also called parametric VaR or variance-covariance VaR), the historical VaR simulation model, and the Monte Carlo VaR model (see Alexander [40] for a readable account).
In random-effects linear models, the normal distribution is often used for the conditional distribution of the response even in cases where the response is a discrete variable with a limited number of possible values.
Previous studies on SNP genotyping array data [ 4, 21] suggested the use of Hidden Markov Models (HMM) to predict CNA and LOH events with a parameter training procedure, which inherently models the normal contamination.
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The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters.
In the topology optimization model, the normal velocities defined on the level set points are considered as design variables.
In the non-linear model, the normal force appears as multiplicative of velocity terms up to cubic order.
Based on the features extracted from normal bearing vibration signals, an HMM was trained to model the normal bearing operating condition.
According to this chronological model, the normal process of flower development was monitored on plants assigned to the experimental control (EVP-treatment) group (Fig. 2a).
Instead of aiming to predict events by identifying typical data patterns they suggest to model the "normal state" of a system.
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