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.

翻译：基于决策导向的模型不确定性视角拓展了传统统计学关于管理、比较和整合多模型推断的思维。贝叶斯预测决策综合（BPDS）推进了概念与理论基础，并定义了将决策分析结果明确整合到候选模型评估、比较及潜在组合中的新方法论。BPDS拓展了近期基于贝叶斯预测综合与经验性目标导向模型不确定性分析的理论与实践进展。这一发展源于在预测决策场景下对模型权重问题提出新颖的主观贝叶斯视角。应用实例涵盖回归预测的最优设计以及金融投资组合决策中的序贯时间序列预测。