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Activity Number: 107 - The ABC of Making an Impact
Type: Invited
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #300059 Presentation
Title: Loss-Based Bayesian Prediction
Author(s): David Frazier* and Gael Martin and Ruben Loaiza-Maya
Companies: Monash University and Monash University and Monash University
Keywords: Forecasting; Bayesian Computation; Approximate Bayesian Computation; M-open; Prediction; Bayesian Computation

Bayesian predictive distributions quantify uncertainty about out-of-sample values of a random process conditional only on observed data; uncertainty regarding model-specific parameters being integrated out via the usual probability calculus. While immensely useful, application of Bayesian prediction is model specific and its statistical validity is most relevant under the so-called M-closed worlds interpretation of model specification. Herein, we propose a novel method for constructing Bayesian predictive distributions that explicitly acknowledges that practitioners operate in an M-open world. This new approach is not based upon a given model, but is instead driven by a user-supplied concept of predictive performance loss. To develop such machinery in the Bayesian paradigm, we rely on the principles underlying approximate Bayesian computation. Specifically, construction of prediction distributions is carried out using simulation, summary statistics that minimize predictive loss over a pre- specified training period, and a tolerance level that captures our risk aversion to predictive loss.

Authors who are presenting talks have a * after their name.

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