Variational Prediction
This addresses computational bottlenecks in Bayesian inference for practitioners, though it appears incremental as it builds on existing variational methods.
The paper tackles the computational intractability of Bayesian posterior predictive distributions by introducing variational prediction, a method that directly learns a variational approximation to the posterior predictive distribution, eliminating test-time marginalization costs, as demonstrated on a toy example.
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.