I am new to probabilistic programming and Bayesian inference, so I apologize in case that question is trivial or lacks clarity. I searched the forum for similar questions but couldn’t find an answer.
Let us consider a problem where we want to sample from
X is the input,
D is the training dataset, and
Y is the expected output. Also, let us assume that we are using a Bayesian Neural Network (BNN) as the model, and a guide whose weights I will refer to as
theta here. After inference with
SVI, my understanding is that
Predictive(model, guide, num_samples,...) will approximate the following integral by averaging
num_samples samples of
Now, if I’m not mistaken, the variance of
P(theta|D) can be considered as the epistemic uncertainty, and the aleatoric uncertainty is encoded in the distribution
Predictive returns samples from
P(Y|X,D), and approximating the variance of those predictions (e.g. via sample variance) means that I lose the ability to distinguish between epistemic and aleatoric uncertainty.
Is there any way to recover he uncertainty from
P(theta|D), which (I think) is the uncertainty of the guide? Thank you very much for your help.
Edit: After playing around, it seems that
pyro.param can be used for retrieving what I’m looking for. For example, if the guide is
pyro.param("AutoDiagonalNormal.scale") seems to provide what I need. However I am unsure, so confirmation of this still would be very much appreciated.