Variational Prediction
Alexander A. Alemi Ben Poole
Bayesian inference offers benefits over maximum likelihood, but it also comes
with computational costs. Computing the posterior is typically intractable, as
is marginalizing that posterior to form the posterior predictive distribution.
In this paper, we present variational prediction, a technique for directly
learning a variational approximation to the posterior predictive distribution
using a variational bound. This approach can provide good predictive
distributions without test time marginalization costs. We demonstrate
Variational Prediction on an illustrative toy example.