Akaike Information Criteria
时间: 2024-06-07 17:06:46 浏览: 8
Akaike Information Criteria (AIC) is a statistical measure used to evaluate the relative quality of statistical models for a given set of data. It was first introduced by the Japanese statistician Hirotugu Akaike in the 1970s.
AIC is based on the principle of parsimony, which states that a model should be as simple as possible while still accurately representing the data. AIC takes into account the number of parameters in a model and the likelihood of the data given the model. The AIC value for a particular model is calculated as:
AIC = -2ln(L) + 2k
where L is the likelihood of the data given the model, and k is the number of parameters in the model. The lower the AIC value, the better the model is considered to be.
AIC is often used in model selection, where several models are compared to determine which one has the best fit to the data. AIC is particularly useful when comparing models that have a different number of parameters or that are not nested within each other.
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