I'm an independent researcher working on a pet project. The general idea is to use a Gaussian Process nonlinear autoregressive model to do forecasting.
I hope to work up to the larger model by building (and understanding) simpler ones. So, I thought I would start by writing a tutorial for variations on Poisson GP regression.
Perhaps such a tutorial would be useful to other new Edward users, beside myself. If anyone is interested in assisting, let me know. I would make a Jupyter notebook, and might use both AutoGrad and Edward (TensorFlow) to highlight differences and similarities. I want to make sure I understand what is happening under the hood.
For inference, I'd like to use a Gaussian mean field for q(), a normalizing flow over Gaussian mean fields, and a likelihood-free variational approach.
I have zero experience with Edward, so I'd be grateful for assistance.