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