论文标题
贝叶斯预测决策综合
Bayesian Predictive Decision Synthesis
论文作者
论文摘要
对模型不确定性的决策观点扩展了有关管理，比较和结合模型集的推论的传统统计思维。贝叶斯预测决策综合（BPD）提高了概念和理论基础，并定义了将决策分析结果明确整合到候选模型的评估，比较和潜在组合中。 BPD扩展了基于贝叶斯预测合成和以经验目标模型不确定性分析的最新理论和实践进步。这是通过在预测决策设置中对模型加权的一种新颖的主观贝叶斯观点的发展来启发的。插图来自应用上下文，包括用于回归预测的最佳设计和为财务组合决策预测的顺序时间序列。
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.
