In machine learning, an agent needs to estimate uncertainty to efficiently
explore and adapt and to make effective decisions. A common approach to
uncertainty estimation maintains an ensemble of models. In recent years,
several approaches have been proposed for training ensembles, and conflicting
views prevail with regards to the importance of various ingredients of these
approaches. In this paper, we aim to address the benefits of two ingredients --
prior functions and bootstrapping -- which have come into question. We show
that prior functions can significantly improve an ensemble agent's joint
predictions across inputs and that bootstrapping affords additional benefits if
the signal-to-noise ratio varies across inputs. Our claims are justified by
both theoretical and experimental results.

本文将探讨模型集成中两个重要因素 -- 先验函数和自助法 -- 的作用，提出了先验函数可以显著提高集成模型在输入上的预测能力且自助法可以在信号 - 噪声比变化时带来额外的好处。通过理论和实验结果，作者证明了这两个元素对于提高机器学习代理的表现和决策效果是必不可少的。