Bayesian Predictive Decision Synthesis

TL;DR

Bayesian Predictive Decision Synthesis (BPDS) introduces a new Bayesian framework that explicitly incorporates decision outcomes into model evaluation and combination, enhancing predictive inference in applied contexts.

Contribution

BPDS advances the theoretical foundation of model uncertainty analysis by integrating decision-analytic outcomes into Bayesian predictive synthesis methodology.

Findings

Demonstrates BPDS in regression prediction and financial forecasting.

Provides a novel subjective Bayesian approach to model weighting.

Enhances decision-making under model uncertainty.

Abstract

Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian predictive synthesis and empirical goal-focused model uncertainty analysis. This is enabled by the development of a novel subjective Bayesian perspective on model weighting in predictive decision settings. Illustrations come from applied contexts including optimal design for regression prediction and sequential time series forecasting for financial portfolio decisions.