This study investigates Bayesian ensemble learning for improving the quality of decision-making. We consider a decision-maker who selects an action from a set of candidates based on a policy trained using observations. In our setting, we assume the existence of experts who provide predictive distributions based on their own policies. Our goal is to integrate these predictive distributions within the Bayesian framework. Our proposed method, which we refer to as General Bayesian Predictive Synthesis (GBPS), is characterized by a loss minimization framework and does not rely on parameter estimation, unlike existing studies. Inspired by Bayesian predictive synthesis and general Bayes frameworks, we evaluate the performance of our proposed method through simulation studies.

翻译：本研究探讨贝叶斯集成学习在提升决策质量方面的应用。我们考虑决策者基于观测数据训练得到的策略，从候选动作集合中选择行动。在此设定下，我们假设存在若干专家，他们根据各自策略提供预测分布。我们的目标是在贝叶斯框架下整合这些预测分布。所提出的广义贝叶斯预测合成方法（GBPS）以损失最小化框架为特征，与现有研究不同，该方法不依赖于参数估计。受贝叶斯预测合成与广义贝叶斯框架的启发，我们通过仿真研究评估了所提出方法的性能。