[D] Deep ensembles as bayesian compared to MC-Dropout

It is known that MC-dropout provide a cheap and fast way to approximate the posterior distribution. the quality of such approximation has been criticized by different autohrs ( osband [http://bayesiandeeplearning.org/2016/papers/BDL\_4.pdf](http://bayesiandeeplearning.org/2016/papers/BDL_4.pdf) , folgoc [https://arxiv.org/pdf/2110.04286.pdf](https://arxiv.org/pdf/2110.04286.pdf) , [https://arxiv.org/pdf/2008.02627.pdf](https://arxiv.org/pdf/2008.02627.pdf) )

the idea is that the 1) approximating the posterior with a bunch of deltas is not enough to extract meaningful information from the posterior itself. 2) the uncertainty does not concentrates with observed data so that in the limit of infinite data goes to …

data dropout extract information machinelearning posterior rate uncertainty