A MULTIDIMENSIONAL UNFOLDING METHOD BASED ON BAYES THEOREM
A MULTIDIMENSIONAL UNFOLDING METHOD BASED ON BAYES THEOREM
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DOI：
10.1016/0168-9002(95)00274-x
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发表时间：
1995-08-15
影响因子：
1.4
通讯作者：
DAGOSTINI, G
中科院分区：
文献类型：
--
作者：
DAGOSTINI, G
贝叶斯定理提供了一种自然的方法来展开实验分布，以便获得对真实分布的最佳估计。贝叶斯方法的弱点，即需要知道初始分布，可以通过迭代过程来克服。由于本文提出的方法不是利用连续变量，而是简单地利用真实值和实测值空间中的单元格，因此它可以应用于多维问题。
Bayes' theorem offers a natural way to unfold experimental distributions in order to get the best estimates of the true ones. The weak point of the Bayes approach, namely the need of the knowledge of the initial distribution, can be overcome by an iterative procedure. Since the method proposed here does not make use of continuous variables, but simply of cells in the spaces of the true and of the measured quantities, it can be applied in multidimensional problems.