Loss-Based Variational Bayes Prediction

• Tác giả: David T Frazier, Bonsoo Koo, Ruben Loaiza-Maya, Gael M Martin
• Ngôn ngữ: eng
• Ký hiệu phân loại: 614.472 Forensic medicine; incidence of injuries, wounds, disease; public preventive medicine
• Thông tin xuất bản: 2021
• Bộ sưu tập: Metadata
• ID: 166845

Tóm tắt

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.