EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0488
Title: Loss-based variational Bayes prediction Authors:  David Frazier - Monash University (Australia)
Ruben Loaiza-Maya - Monash University (Australia) [presenting]
Gael Martin - Monash University (Australia)
Bonsoo Koo - Monash University (Australia)
Abstract: A new method is proposed for 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 loss-based, or Gibbs, posterior that is directly focused on predictive accuracy. The theoretical behavior of the new prediction approach is analyzed and a form of optimality is demonstrated. Applications to both simulated and empirical data using high-dimensional Bayesian neural networks and autoregressive mixture models demonstrate that the approach provides more accurate results than various alternatives, including misspecified likelihood-based predictions.