When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a normal (or Gaussian) distribution. For example, suppose the training data contains a continuous attribute, formula_22. We first segment the data by the class, and then compute the mean and variance of formula_22 in each class. Let formula_24 be the mean of the values in formula_22 associated with class "Ck", and let formula_26 be the Bessel corrected variance of the values in formula_22 associated with class "Ck". Suppose we have collected some observation value formula_28. Then, the probability "density" of formula_28 given a class formula_3, formula_31, can be computed by plugging formula_28 into the equation for a normal distribution parameterized by formula_24 and formula_26. That is,