Under this assumption, the effect of each factor can be linear, quadratic or of higher order, but the model assumes that there exists no cross product effects (interactions) among the individual factors. That means the effect of independent variable 1 on performance parameter does not depend on the different level settings of any other independent variables and vice versa. If at anytime, this assumption is violated, then the additivity of the main effects does not hold, and the variables
One of them is to discard patients with incomplete sequences, and then analyze only the units with complete data. Methods that use this approach are called deletion methods. These methods do not replace or impute dropouts and do not make other adjustments to account for dropout. They share many properties in terms of dropout mechanisms and the ineﬃciencies inherent in losing data for statistical power, although not all to the same degree. The main advantage of these techniques is their simplicity, and the ease with which they can be applied using much of the standard statistical softwares.
(2) Strategy for noise: When observed data are measured, we may often think that the data were disturbed by random noise. In this case, a fuzzification operator should convert the probabilistic data into fuzzy numbers. In this way, computational efficiency is enhanced since fuzzy numbers are much easier to manipulate than random variables. Otherwise, we assume that the observed data do not contain vagueness,and then we consider the observed data as a fuzzy singleton. A fuzzy singleton is a precise value and hence no fuzziness is introduced by fuzzification in this case.
Thus, EB uses Bayesian approach for modelling a situation and conventional approach for constructing estimators of unknown quantities of the model. The EB estimation approach has facilitated the analysis of complex multi-faceted problems, which are often difficult to handle using the conventional or Bayesian approach. Broadly, EB approach has two essential components, likelihood function and prior distribution. Once the data has been observed, the sampling distribution can be considered as the function of unknown prior parameter. The likelihood function is constructed from the sampling distribution of the data.
In order to recognize and classify the situations, the technique must be able to handle uncertainty and have an easy way of modeling the situations. One extreme, neural nets, has the obvious ability to recognize situations. Neural nets starts with no knowledge at all, and learns from training data. The drawback is that the net has to be adequately trained, and lack of training data is always a big problem. The other extreme, a forward-chained expert system, has to be modeled by an expert and cannot update its knowledge automatically.
SAA  is a sampling based approach that can be applied to solve the SCMP (i.e. model (3)-(12)). Since the objective function (∑_(ξ∈Ξ)▒∑_(p∈P)▒〖Φ(ξ) .o_p .x_p^ξ 〗) cannot directly be optimized, the sample average is maximized instead of the original value. The expected value could be written as: E_(ξ∈Ξ) [∑_(p∈P)▒〖o_p .x_p^ξ 〗]. While directly computing the expected value is not possible for most problems, it can be approximated through Monte Carlo sampling in some situations.
Naive Bayes Classifier A Naïve Bayes Classifier is a simple probalistic classifier based on applying Bayes theorem having strong(Naive) independence assumptions. A Naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. Naive Bayes Classifiers can be trained very efficiently in a supervised learning setting because they depend on the probability model.This format is very much ambiguous for requirement specifications so it is hard to identify consistencies. A method is used for requirements specifications documents having similar contents to each other through a hierarchical text classification. This method has two main classification process: heavy classification and light classification.
PSO may be machine intelligence-based technique that’s not mostly affected by the size and nonlinearity of the matter, and may converge to the optimum answer in several issues .This Hybrid PSO has been used to solve the maximum loadability problem . This paper doesn't contemplate the voltage limit and this rule isn't appropriate for large scale system (limited to fourteen bus system). Multi agent hybrid PSO (MAHPSO) have been developed and applied to work out the maximum loadability limit in . This MAHPSO has benefits of each HPSO and MAPSO. The optimum allocation of generators at this maximum loading purpose is set in .