Variational Prediction

The paper introduces a new method for attempting to learn variational approximations to Bayesian posterior predictive distributions that doesn’t require (1) the posterior predic- tive distribution itself, (2) the posterior distribution (3) exact samples from the posterior (4) or any test time marginalization.

• Machine Intelligence

• Algorithms and Theory