论文标题
综合有机推理（IOI）：统计范式的对帐
Integrated organic inference (IOI): A reconciliation of statistical paradigms
论文作者
论文摘要
人们认识到，贝叶斯的推论方法无法充分应对所有关于人群兴趣量的前总数据的类型，通常是在实践中持有的。特别是，当对模型的某些或所有参数或相关参数空间的某些分区中缺乏这种信念时，通常会遇到困难。为了解决这个问题，提出了一个相当全面的推论理论，称为综合有机推理，该理论基于费舍里亚和贝叶斯推理的融合。根据对任何给定模型参数的持有的DATA知识，使用三种推理方法之一，即有机基金推断，双疗法推理和贝叶斯推论，对所有其他参数的参数进行了推论。然后，由这样做的完整条件后数据密度使用一个框架组合，该框架允许合理地形成所有参数的联合DATA密度，而无需这些完整的条件密度兼容。提出了该理论应用的各种示例。最后，该理论是根据先前被定义为普遍的主观概率部分辩护的。
It is recognised that the Bayesian approach to inference can not adequately cope with all the types of pre-data beliefs about population quantities of interest that are commonly held in practice. In particular, it generally encounters difficulty when there is a lack of such beliefs over some or all the parameters of a model, or within certain partitions of the parameter space concerned. To address this issue, a fairly comprehensive theory of inference is put forward called integrated organic inference that is based on a fusion of Fisherian and Bayesian reasoning. Depending on the pre-data knowledge that is held about any given model parameter, inferences are made about the parameter conditional on all other parameters using one of three methods of inference, namely organic fiducial inference, bispatial inference and Bayesian inference. The full conditional post-data densities that result from doing this are then combined using a framework that allows a joint post-data density for all the parameters to be sensibly formed without requiring these full conditional densities to be compatible. Various examples of the application of this theory are presented. Finally, the theory is defended against possible criticisms partially in terms of what was previously defined as generalised subjective probability.
