Information of interest can often only be extracted from data by model fitting. When the functional form of such a model can not be deduced from first principles, one has to make a choice between different possible models. A common approach in such cases is to minimise the information loss in the model by trying to reduce the number of fit variables (or the model flexibility, respectively) as much as possible while still yielding an acceptable fit to the data. Model selection via the Akaike Information Criterion (AIC) provides such an implementation of Occam's razor. We argue that the same principles can be applied to optimise the penalty-strength of a penalised maximum-likelihood model. However, while in typical applications AIC is used to choose from a finite, discrete set of maximum-likelihood models the penalty optimisation requires to select out of a continuum of candidate models and these models violate the maximum-likelihood condition. We derive a generalised information criterion AICp that encompasses this case. It naturally involves the concept of effective free parameters which is very flexible and can be applied to any model, be it linear or non-linear, parametric or non-parametric, and with or without constraint equations on the parameters. We show that the generalised AICp allows an optimisation of any penalty-strength without the need of separate Monte-Carlo simulations. As an example application, we discuss the optimisation of the smoothing in non-parametric models which has many applications in astrophysics, like in dynamical modeling, spectral fitting or gravitational lensing.
翻译:感兴趣的信息往往只能通过模型安装从数据中提取。 当这种模型的功能形式无法从最初的原则中推导出时, 就必须在不同的可能的模式中做出选择。 在这类情况下, 一种共同的方法是尽量减少适合变量的数量( 或模型灵活性), 尽量减少模型中的信息损失, 同时仍能产生一个可接受的数据。 通过 Akaike Inform Struition (AIC) 进行的模型选择提供了奥卡姆剃刀的这种执行。 我们争辩说, 同样的原则可以适用于优化惩罚性最高接近性中最接近的模型的非惩罚强度。 但是, 在典型的应用中, AIC 通常采用从有限、 离散、 最接近性模型中选择最差的信息损失, 而这些模型则违反最大相似性条件。 我们从一个通用的信息标准 AICp 中包含了这个案例。 我们自然会包含一个有效的自由参数概念, 这个概念非常灵活, 可以适用于任何模型, 并且可以应用一个惩罚性最接近性的模型, 在不进行线性或非线性的最佳模型中选择性模型, 或不以普通的软度模型来显示一个限制性模型。