The work related to this paper falls into two sub topics, described as follows.
gal and ghahramani used dropout in DNNs as an approximate Bayesian inference in deep gaussian processes (gal and ghahramani, 2016) to mitigate the problem of representing uncertainty in deep learning without sacrificing the computational complexity.