 The reason is that models derived directly from the "n"-gram frequency counts have severe problems when confronted with any "n"-grams that have not explicitly been seen before – the zero-frequency problem.Various smoothing methods are used, from simple "add-one" (Laplace) smoothing (assign a count of 1 to unseen "n"-grams; see Rule of succession) to more sophisticated models, such as Good–Turing discounting or back-off models.Some of these methods are equivalent to assigning a prior distribution to the probabilities of the "n"-grams and using Bayesian inference to compute the resulting posterior "n"-gram probabilities.