 "n"-gram models are now widely used in probability, communication theory, computational linguistics (for instance, statistical natural language processing), computational biology (for instance, biological sequence analysis), and data compression.Two benefits of "n"-gram models (and algorithms that use them) are simplicity and scalability – with larger "n", a model can store more context with a well-understood space–time tradeoff, enabling small experiments to scale up efficiently.Figure 1 shows several example sequences and the corresponding 1-gram, 2-gram and 3-gram sequences.