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.

• Algorithms and Theory

• Machine Intelligence