Loss-Based Variational Bayes Prediction

David T. Frazier; Ruben Loaiza-Maya; Gael M. Martin; Bonsoo Koo

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arXiv · 2104.14054

Loss-Based Variational Bayes Prediction

David T. Frazier·Ruben Loaiza-Maya·Gael M. Martin·Bonsoo Koo

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Abstract

We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional (but parametric) predictive models, this new approach constructs a posterior predictive using a variational approximation to a generalized posterior that is directly focused on predictive accuracy. The theoretical behavior of the new prediction approach is analyzed and a form of optimality demonstrated. Applications to both simulated and empirical data using high-dimensional Bayesian neural network and autoregressive mixture models demonstrate that the approach provides more accurate results than various alternatives, including misspecified likelihood-based predictions.

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BibTeX RIS

David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin, Bonsoo Koo. 2021-04-29. Loss-Based Variational Bayes Prediction. https://arxiv.org/abs/2104.14054

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