  In this report, we present qualitative analysis of Monte Carlo (MC) dropout
method for measuring model uncertainty in neural network (NN) models. We first
consider the sources of uncertainty in NNs, and briefly review Bayesian Neural
Networks (BNN), the group of Bayesian approaches to tackle uncertainties in
NNs. After presenting mathematical formulation of MC dropout, we proceed to
suggesting potential benefits and associated costs for using MC dropout in
typical NN models, with the results from our experiments.
