Gaussian naive Bayes 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, . The data is first segmented by the class, and then the mean and variance of is computed in each class. Let be the mean of the values in associated with class , and let be the Bessel corrected variance of the values in associated with class . Suppose one has collected some observation value . Then, the probability density of given a class , i.e., , can be computed by plugging into the equation for a normal distribution parameterized by and . Formally,
Gaussian naive Bayes 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, . The data is first segmented by the class, and then the mean and variance of is computed in each class. Let be the mean of the values in associated with class , and let be the Bessel corrected v,ariance of the values in associated with class . Suppose one has collected some observation value . Then the probability density of given a class , i.e., , can be computed by plugging into the, equation for a normal distribution parameterized by and . Formally