贝叶斯综合：结合主观分析，并应用于臭氧数据
Bayesian Synthesis: Combining subjective analyses, with an application to ozone data
摘要：
贝叶斯模型平均使人们能够以连贯的方式组合多个模型的不同预测，从而获得卓越的预测性能。性能的提高来自于对做出不同预测的模型进行平均。在这项工作中，我们利用可能是不同预测的最大驱动力-不同的分析师-以获得模型平均的全部好处。在我们的方法的标准实现中，几个数据分析师独立地处理数据集的各个部分，得出单独的模型，这些模型最终通过特定的加权方法进行更新和组合。我们称这个建模过程为贝叶斯综合。该方法有助于减轻人们对贝叶斯范式的基本基础与贝叶斯统计实践之间存在巨大差距的担忧。在实验工作中，我们表明，人类建模的预测性能优于许多自动建模技术，包括AIC，BIC，平滑样条，CART，袋装CART，贝叶斯CART，BMA和LARS，只有略逊于BART。我们还表明，贝叶斯合成进一步提高了预测性能。此外，我们检查了分析师之间的简单平均值的预测性能，我们称之为凸合成，并发现它也产生了改进。
英文摘要：
Bayesian model averaging enables one to combine the disparate predictions of a number of models in a coherent fashion, leading to superior predictive performance. The improvement in performance arises from averaging models that make different predictions. In this work, we tap into perhaps the biggest driver of different predictions---different analysts---in order to gain the full benefits of model averaging. In a standard implementation of our method, several data analysts work independently on portions of a data set, eliciting separate models which are eventually updated and combined through a specific weighting method. We call this modeling procedure Bayesian Synthesis. The methodology helps to alleviate concerns about the sizable gap between the foundational underpinnings of the Bayesian paradigm and the practice of Bayesian statistics. In experimental work we show that human modeling has predictive performance superior to that of many automatic modeling techniques, including AIC, BIC, Smoothing Splines, CART, Bagged CART, Bayes CART, BMA and LARS, and only slightly inferior to that of BART. We also show that Bayesian Synthesis further improves predictive performance. Additionally, we examine the predictive performance of a simple average across analysts, which we dub Convex Synthesis, and find that it also produces an improvement.
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