AIC和Akaike信息标准（AIC）是对给定数据集的统计模型的相对质量的度量。给定数据的模型集合，AIC估计每个模型相对于其他每个模型的质量。因此，AIC为模型选择提供了一种手段。AIC建立在信息论的基础上：当一个给定的模型被用来表示生成数据的过程时，它提供了对信息丢失的相对估计。在此过程中，它处理模型的拟合优度和模型复杂性之间的权衡。AIC不提供一个测试模型的意义上测试一个无效的假设，即AIC可以告诉任何关于模型的质量在绝对意义上。如果所有的候选模型都不适合，AIC将不会给出任何警告。
AIC & MDL The Akaike information criterion (AIC) is a measure of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Hence, AIC provides a means for model selection. AIC is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. In doing so, it deals with the trade-off between the goodness of fit of the model and the complexity of the model. AIC does not provide a test of a model in the sense of testing a null hypothesis i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.
资源文件列表
AIC_MDL.m , 1421