Loss-Based Variational Bayes Prediction

David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin and Bonsoo Koo
Authors registered in the RePEc Author Service: Rubén Albeiro Loaiza Maya

Papers from arXiv.org

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.

Date: 2021-04, Revised 2022-05
New Economics Papers: this item is included in nep-big, nep-ecm and nep-ets
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Citations: View citations in EconPapers (4)

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http://arxiv.org/pdf/2104.14054 Latest version (application/pdf)

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Working Paper: Loss-Based Variational Bayes Prediction (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.14054

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