AI 中文总结
该研究推导了与决策问题相关的差异、不确定性和相关性的相干测度理论，并将其应用于贝叶斯预测实验设计，证实基于任意相干函数的选择准则可得到相同解。
AI 中文摘要
我们展示了如何结合任意决策问题，推导测量分布的不确定性、差异和相关性的相关函数。这类“相干”函数具有我们所刻画的特殊性质，且每个函数本质上可决定其他函数。该理论被应用于选择实验以进行后续预测的贝叶斯问题中，研究表明，相干选择准则可基于任意相干函数构建，相关函数会产生相同的解。
英文摘要
We show how, associated with any decision problem, we may derive related functions measuring uncertainty, discrepancy and dependence of distributions. Such "coherent" functions have special properties, which we characterise, and each function essentially determines the others. The theory is applied to the Bayesian formulation of the problem of choosing an experiment in order to make a subsequent prediction. It is shown that coherent choice criteria may be based on any of the coherent functions, with related functions yielding identical solutions.
CommentsThis is an old unpublished paper--Research report 139 (1994) of the Department of Statistical Science, University College London, updated 1998--that many people have asked to be made available online
