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.

翻译：贝叶斯推断相比最大似然估计具有优势，但也伴随着计算成本。计算后验分布通常难以处理，而边缘化该后验以形成后验预测分布同样存在困难。本文提出变分预测（variational prediction）方法，这是一种通过变分界直接学习后验预测分布的变分逼近技术。该方法能在无需测试时边缘化计算成本的情况下，提供良好的预测分布。我们通过一个说明性实例演示了变分预测的有效性。