整合有机推断（IOI）：统计范式的调和
Integrated organic inference (IOI): A reconciliation of statistical paradigms
摘要：
人们认识到，贝叶斯推理方法不能充分处理所有类型的前数据信念的人口数量的利益，通常在实践中举行。特别是，它通常遇到困难时，有一个缺乏这样的信念在一些或所有参数的模型，或在某些分区的参数空间有关。为了解决这个问题，一个相当全面的推理理论被称为集成有机推理，它是基于融合的费舍尔和贝叶斯推理。根据关于任何给定模型参数所持有的先验数据知识，使用三种推断方法之一来对所有其他参数进行关于该参数的条件性推断，即有机信念推断、双空间推断和贝叶斯推断。然后，使用允许所有参数的联合后数据密度被合理地形成而不需要这些全条件密度兼容的框架来组合由此产生的全条件后数据密度。各种例子，这一理论的应用。最后，该理论是捍卫可能的批评部分在什么以前被定义为广义的主观概率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
