Autor:	Alemi, Alexander A., Poole, Ben
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: AABI2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.07568 Zobrazit plný text záznamu View this record from Arxiv