In this paper the problem of learning appropriate bias for an environment of
related tasks is examined from a Bayesian perspective. The environment of
related tasks is shown to be naturally modelled by the concept of an {\em
objective} prior distribution. Sampling from the objective prior corresponds to
sampling different learning tasks from the environment. It is argued that for
many common machine learning problems, although we don't know the true
(objective) prior for the problem, we do have some idea of a set of possible
priors to which the true prior belongs. It is shown that under these
circumstances a learner can use Bayesian inference to learn the true prior by
sampling from the objective prior. Bounds are given on the amount of
information required to learn a task when it is simultaneously learnt with
several other tasks. The bounds show that if the learner has little knowledge
of the true prior, and the dimensionality of the true prior is small, then
sampling multiple tasks is highly advantageous.

本文从贝叶斯角度探讨了在相关任务环境下学习适当的偏差问题，并展示了环境的概念是自然地模拟了任务的目标先验分布。本文论证了对于许多常见的机器学习问题，虽然我们不知道问题的真正（客观）先验分布，但我们可以对真正的先验分布可能属于的一组可能的先验分布有一些想法。在这些情况下，本文展示了学习者可以使用贝叶斯推断通过从客观先验中进行采样来学习真实先验分布。当同时学习多个任务时，给出了学习任务所需信息量的下限。这些下限表明，如果学习者很少了解真实先验分布，且真实先验分布的维数很小，则采样多个任务是非常有优势的。