Pytorch implementaiton of a Neural Network with Variational Inference using Bayes by Backprop and MC Dropout algorithms

• Flexible implementations with the desired structure and priors

• Priors and noise can be learned from the data

• Homo- and heteroscedastic uncertainties can be quantified