Bayesian posterior approximation with stochastic ensembles

Oleksandr Balabanov, Bernhard Mehlig, Hampus Linander

We introduce ensembles of stochastic neural networks to approximate the Bayesian posterior, combining stochastic methods such as dropout with deep ensembles. The stochastic ensembl…