Bagging or bootstrap aggregation is a special case of model averaging. Given a standard training set bagging generates $m$ new training sets by bootstrapping, and then the results of using some training method on the $m$ generated data sets are averaged. Bagging can stabilize results from some unstable methods such as trees.
See https://en.wikipedia.org/wiki/Bootstrap_aggregating for more information and references.
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