I'm learning about dropout from these sources:
At test time, the trained weights are scaled by a factor of $p$, to replace them with their mean. According to the original paper this is kind of like an approximation to an ensemble of the $2^n$ possible dropout models that "works well in practice".
It's an approximation because the network isn't a linear function of its weights.
Is there any justification for this approximation?
It's not even clear to me that dropout is a consistent estimator.