Model selection in the average of inconsistent data: an analysis of the measured Planck-constant values
Model selection in the average of inconsistent data: an analysis of the measured Planck-constant values
复制标题
不一致数据平均值中的模型选择：测量的普朗克常数值的分析
DOI：
10.1088/0026-1394/49/4/492
复制
发表时间：
2012
期刊：
影响因子：
2.4
通讯作者：
M. Predescu
中科院分区：
文献类型：
--
作者：
Giovanni Mana;E. Massa;M. Predescu
当数据不符合已知抽样方差的假设时，用一个常数拟合一组测量值是一个长期争论的问题。给定数据，拟合将需要找到最值得信赖的被测量值。贝叶斯推理在这里审查，分配概率可能的被测量值。通过贝叶斯模型比较检验了关于数据方差的不同假设。最后，模型选择以推导普朗克常数的估计为例。
When the data do not conform to the hypothesis of a known sampling variance, the fitting of a constant to a set of measured values is a long debated problem. Given the data, fitting would require one to find what measurand value is the most trustworthy. Bayesian inference is reviewed here, to assign probabilities to the possible measurand values. Different hypotheses about the data variance are tested by Bayesian model comparison. Eventually, model selection is exemplified in deriving an estimate of the Planck constant.