Hi. In pyro I can use Delta distributions throughout the guide (surrogate posterior) to learn point estimates for parameters, as opposed to learning more complicated distributions with variances. This is helpful when I find guide posteriors with variances fail to properly approximate the model.
Is there an equivalent approach in TensorFlow? I couldn’t find anything when Googling for point estimation in TensorFlow.