is it possible to use a bayesian neural network to calculate the covariance matrix among a set of predicted values? as title says, i have created a little bayesian fcnn with dropout layers trained on some cosmological data that come with a covariance matrix that is  almost 0 everywhere. My goal was to use the NN to predict some  continuous values in a range where data are scarce (it's a regression case), but i also need the covariance matrix among the predicted values. I was wondering if there was a way to calculate it. wikipedia https://en.wikipedia.org/wiki/Estimation_of_covariance_matrices gives a formula for the estimate of covariance but i don't know if it's valid for the result predicted by a neural network , especially one with dropout layers turned on. thanks

Yes, I am using a Bayesian Neural Network built with Pymc3 for excess return prediction and then I calculate the variance covariance matrix from the prediction. But in my case it is a binary classification task where the target is 1 when the return is larger than the 10th percentile and 0 otherwise.
