r/MachineLearning - [D] Is this a valid description of Bayesian Deep Learning?

The other answer here just posted text from an article on Medium. It goes over the idea of Bayesian deep networks, and lists three ways of implementing a Bayesian approach to network parameters. The first is to use Monte Carlo -- which means you have to first sample the network parameters (weights and biases), and then sample the network outputs from the inputs. That will never work at scale; you can't train anything practical that way, too slow. The second approach is to use variational inference to approximately find the right weights; but you still have to sample the weights and average in order to get the mean and variance for the network outputs, which still slows down inference, without mentioning that variational inference is approximate and often very computationally expensive. The third approach is the one that was actually proposed, that is, to use DropOut, which is hardly Bayesian in the traditional sense, whatever theoretical justification may be offered.