The general equations of an n-gram model apply the Markov assumption to estimate word probabilities. The conditional probability for the next word is approximated by looking $N-1$ words into the past:

$P(w_n|w_{1:n-1}) \approx P(w_n|w_{n-N+1:n-1})$

The probability of a complete word sequence is approximated as the product of these conditional probabilities:

$P(w_{1:n}) \approx \prod^n_{k=1}P(w_k|w_{k-N+1:k-1})$