Context mixing

Context mixing is a type of data compression algorithm in which the next-symbol predictions of two or more statistical models are combined to yield a prediction that is often more accurate than any of the individual predictions. For example, one simple method (not necessarily the best) is to average the probabilities assigned by each model. Combining models is an active area of research in machine learning.

The PAQ series of data compression programs use context mixing to assign probabilities to individual bits of the input.