Editing Context mixing (section)

'''Context mixing''' is a type of [[data compression]] [[algorithm]] in which the next-[[symbol]] predictions of two or more [[statistical model]]s 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 [[Mathematical model|model]]. The [[random forest]] is another method: it outputs the prediction that is the [[mode (statistics)|mode]] of the predictions output by individual models.  Combining models is an active area of research in [[machine learning]].{{Citation needed|reason=This sentence is subjective, and only valid for certain period of time.|date=February 2015}}

The [[PAQ]] series of [[data compression]] programs use context mixing to assign probabilities to individual [[bit]]s of the input.