variational-inference

Pyro's HMC and NUTS implementations are feature-complete and well-tested, but they are quite slow in models like the one in our Bayesian regression tutorial that operate on small tensors for reasons that are largely beyond our control (mostly having to do with the design and implementation of torch.autograd), which is unfortunate because these

I'm trying to have a multi-dimensional lengthscale for my kernel, and cannot find in the documentation how to do this. The closest I've come is specifying input_dim, as described here, but in version 2.0.5 I get an error that input_dim is an unknown keyword argument. How would I get these multidimensional lengthscales in gpfl

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac

This may be useful for Inference algorithms to use during automatic gradient chaining.