Focused Bayesian prediction
提出一种新贝叶斯预测方法，无需正确指定真实数据生成过程，通过用户指定的预测准确度准则更新先验，在模拟和实证中相比传统似然法有显著准确度提升。
Summary 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.