The assumption that the probability of a word depends only on the previous word is called a Markov assumption. Markov models are the class of probabilistic models Markov that assume we can predict the probability of some future unit without looking too far into the past. We can generalize the bigram (which looks one word into the past) to the trigram (which looks two words into the past) and thus to the n-gram (which n-gram looks n − 1 words into the past). Thus, the general equation for this n-gram approximation to the conditional probability of the next word in a sequence is