 Typically, the "n"-gram model probabilities are not derived directly from frequency counts, because models derived this way have severe problems when confronted with any "n"-grams that have not been explicitly seen before.Instead, some form of smoothing is necessary, assigning some of the total probability mass to unseen words or "n"-grams.Various methods are used, from simple "add-one" smoothing (assign a count of 1 to unseen "n"-grams, as an uninformative prior) to more sophisticated models, such as Good-Turing discounting or back-off models.