More concisely, an "n"-gram model predicts formula_2 based on formula_3. In probability terms, this is formula_4. When used for language modeling, independence assumptions are made so that each word depends only on the last "n" − 1 words. This Markov model is used as an approximation of the true underlying language. This assumption is important because it massively simplifies the problem of estimating the language model from data. In addition, because of the open nature of language, it is common to group words unknown to the language model together.