Prediction is a central task of machine learning. Our goal is to solve large scale prediction problems using Generative Quantile Bayesian Prediction (GQBP).By directly learning predictive quantiles rather than densities we achieve a number of theoretical and practical advantages. We contrast our approach with state-of-the-art methods including conformal prediction, fiducial prediction and marginal likelihood. Our distinguishing feature of our method is the use of generative methods for predictive quantile maps. We illustrate our methodology for normal-normal learning and causal inference. Finally, we conclude with directions for future research.

翻译：预测是机器学习的核心任务。本文旨在通过生成式分位数贝叶斯预测方法解决大规模预测问题。通过直接学习预测分位数而非概率密度，本方法在理论与应用层面均展现出多重优势。我们将该方法与包括保形预测、基准预测及边缘似然在内的前沿技术进行对比。本方法的显著特征在于采用生成式方法构建预测分位数映射。我们通过正态-正态学习与因果推断的案例演示了该方法的应用。最后，本文提出了未来研究的若干方向。