FIC在模型选择中的简单应用
A simple application of FIC to model selection
摘要：
我们最近提出了一种新的基于信息的方法来选择模型，频率信息准则（FIC），调和基于信息和频率推理。本文的目的是提供一个简单的例子，这个标准的应用和示范的自然出现的模型复杂性与AIC样（$N^0$）和BIC样（$\log N$）缩放与观察数$N$。开发的应用程序是故意简化，使分析易于处理。
英文摘要：
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like ($N^0$) and BIC-like ($\log N$) scaling with observation number $N$. The application developed is deliberately simplified to make the analysis analytically tractable.
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。
