This paper reviews the growing field of Bayesian prediction. Bayes point and interval prediction are defined and exemplified and situated in statistical prediction more generally. Then, four general approaches to Bayes prediction are defined and we turn to predictor selection. This can be done predictively or non-predictively and predictors can be based on single models or multiple models. We call these latter cases unitary predictors and model average predictors, respectively. Then we turn to the most recent aspect of prediction to emerge, namely prediction in the context of large observational data sets and discuss three further classes of techniques. We conclude with a summary and statement of several current open problems.

翻译：本文综述了贝叶斯预测这一不断发展的研究领域。首先对贝叶斯点预测与区间预测进行定义与示例说明，并将其置于统计预测的广义框架中进行分析。随后阐述了贝叶斯预测的四类通用方法，并转向预测器的选择问题。预测器的选择可通过预测性途径或非预测性途径实现，且可基于单一模型或多重模型构建——分别称为单一预测器与模型平均预测器。在此基础上，本文探讨了预测领域的最新发展方向，即大规模观测数据集背景下的预测问题，并进一步讨论了三类新兴技术。最后，本文对当前研究进行总结，同时提出了若干待解决的关键问题。