The logic of inductive inference
The logic of inductive inference
复制标题
DOI：
10.2307/2342435
复制
发表时间：
1935-01-01
影响因子：
--
通讯作者：
Fisher, RA
中科院分区：
文献类型：
--
作者：
Fisher, RA
在从样本到从中抽取样本的总体的推理中，数学似然性（与观察到的样本应从给定总体中抽取的后验概率成正比）通常取代概率作为理性信念的度量。当样本量无限制地增加时，总体参数的估计值趋向于该参数的值，该估计值是一致的估计值。 1/nV 的极限（其中 n 是样本大小，V 是估计方差）必然小于或等于量 i，该量与所使用的估计方法无关。如果 l/nV = i 的极限，则估计是有效的。最大似然法可以实现有效的估计。一些被称为充分的估计，即使来自有限的样本，也包含数据提供的全部信息。在其他情况下，可能会计算辅助统计数据，这些统计数据不会告诉我们任何有关参数值的信息，而是告诉我们对参数的估计有多好。
In reasoning from a sample to the population from which it is drawn the mathematical likelihood, which is proportional to the a posteriori probability that the observed sample shall be drawn from a given population, generally takes the place of probability as a measure of rational belief. An estimate of a parameter of the population which, as the size of sample increases without limit, tends to the value of the parameter is a consistent estimate. The limit of 1/nV, where n is size of sample and V is variance of estimate, is necessarily less than or equal to a quantity, i, which is independent of the method of estimation used. If the limit of l/nV = i, the estimate is efficient. The method of maximum likelihood leads to efficient estimates. Some estimates, which are called sufficient, contain, even from finite samples, the whole of the information supplied by the data. In other cases ancillary statistics may be calculated, which tell us nothing about the value of the parameter, but, instead, tell us how good an estimate we have made of it.