Model averaging

On keeping many incorrect hypotheses and using them all as one goodish one

Ensemble methods. A mere placeholder to remind me to create a model averaging notebook, since I’ve seen the idea pop up in disconnected areas recently. specifically a Bayesian heuristic for dropout in neural nets, AIC for frequentist model averaging, and in a statistical learning context for optimal time series prediction.

Relationship to Bayesian posterior predictive distributions?

This seems to not be quite the same thing as bagging, in that when you take a

Model weights are often in terms of degrees-of-freedom penalties. It would probably be an instructive exercise for me to work out why for myself.

Hinton, Vinyals, and Dean (2015) has some work on making a model that already includes several averaged models into itself, somehow. This is apparently called “dark knowledge”.

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