Hi Dustin

I have some confusion about inferring local latent variables by Edward inference, say we have different local latent variables z_n for different groups of datapoints x_n, and all z_n share the same prior distribution with a global latent variable \beta, if I input all datapoints into Inference({z:qz, \beta: q\beta},{x:x_data}), can z_n be updated accordingly by x_n? Or do I need to create different inference for each z_n? I think it should be the former case, but I’m not familiar with tensorflow and didn’t find related documentation.

Sorry to bother you, thanks in advance for your help~!