We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples, we find notable gains in predictive accuracy relative to conventional likelihood-based prediction.

中文翻译：

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聚焦贝叶斯预测

我们提出了一种进行贝叶斯预测的新方法，该方法无需正确指定未知的真实数据生成过程即可提供准确的预测。先验是在一类合理的预测模型上定义的。在观察数据后，我们通过捕获用户指定的预测准确性度量的标准更新这些模型的先验后验。在有规律的情况下，此更新会在预测类的元素上产生后验集中，从而最大化准确度度量的期望。在一系列模拟实验和经验示例中，我们发现相对于传统的基于似然的预测，预测精度有了显着提高。