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- W4384652262 abstract "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." @default.
- W4384652262 created "2023-07-19" @default.
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- W4384652262 date "2023-07-14" @default.
- W4384652262 modified "2023-10-18" @default.
- W4384652262 title "Variational Prediction" @default.
- W4384652262 doi "https://doi.org/10.48550/arxiv.2307.07568" @default.
- W4384652262 hasPublicationYear "2023" @default.
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