  Existing deep neural network (DNN) based wireless localization approaches
typically do not capture uncertainty inherent in their estimates. In this work,
we propose and evaluate variational and scalable DNN approaches to measure the
uncertainty as a result of changing propagation conditions and the finite
number of training samples. Furthermore, we show that data uncertainty is
sufficient to capture the uncertainty due to non-line-of-sight (NLOS) and,
model uncertainty improves the overall reliability. To assess the robustness
due to channel conditions and out-of-set regions, we evaluate the methods on
challenging massive multiple-input multiple-output (MIMO) scenarios.
